Treatment Decision Making in Older Adults Newly Diagnosed with Myeloma
Joseph D. Tariman
A dissertation
submitted in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
University of Washington
2011
Program Authorized to Offer Degree:
School of Nursing
University of Washington
Graduate School
This is to certify that I have examined this copy of a doctoral dissertation by
Joseph D. Tariman
and have found that it is complete and satisfactory in all respects,
and that any and all revisions required by the final
examining committee have been made.
Chair of the Supervisory Committee:
___________________________________________________
Donna L. Berry
Reading Committee:
__________________________________________________
Donna L. Berry
__________________________________________________
Barbara B. Cochrane
__________________________________________________
Ardith Doorenbos
___________________________________________________
Karen G. Schepp
Date: _________________________________
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the University of Washington, I agree that the Library shall make its copies freely available for
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University of Washington
Abstract
Treatment Decision Making in Older Adults Newly Diagnosed with Myeloma
Joseph D. Tariman
Chair of the Supervisory Committee:
Affiliate Professor Donna L. Berry
Department of Biobehavioral Nursing and Health Systems
Myeloma is a plasma cell malignancy of the elderly, affecting over 20,000 individuals
annually. The advent of novel agents has increased the number of treatment options for older
adults newly diagnosed with myeloma, but it has also led to clinical uncertainties. These
uncertainties regarding optimal therapy are due in part to the lack of randomized controlled trials
comparing new treatment options over the existing options. There is considerable evidence that
patients want to be informed and consulted with regard to the impact of treatment, not only on
survival but also on quality of life. However, no data exist regarding the process of treatment
decision making and degree of patients’ control preferences over treatment decisions in older
adults diagnosed with myeloma.
This dissertation study employed a multi-method, cross-sectional survey design.
Physicians and older adults diagnosed with myeloma, ages 60 years and above were interviewed
separately to describe how treatment decisions were made from both perspectives. After the
interview, each participant was asked to report their degree of preferred control over treatment
decisions using the Control Preference Scale (CPS) and to complete the Information Needs
Questionnaire to assess their information priorities.
The results revealed that patient and physician participants have various treatment
considerations to arrive at a possible “best treatment decision.” A high percentage of these older
adults with myeloma want some control of the treatment decision (45% preferred shared and
50% active roles). Their top three priority information needs related to “different types of
treatments” and corresponding advantages/disadvantages, the “likelihood of cure” and “caring
for myself at home.” No statistically significant differences in decisional role preferences and
priorities of information needs were seen among various sociodemographic factors.
Since older adults newly diagnosed with myeloma will most likely prefer to participate in
treatment decision-making, physicians should assess their preferences. Physicians should elicit
from patients their treatment preferences to account for contextual factors that might be
overlooked if their opinions on therapies are not solicited. Health care professionals should
assess and address the priority of information needs of myeloma patients and encourage patients
to express their decisional role preferences to their physician.
TABLE OF CONTENTS
Page
List of Figures ..................................................................................................................... iii
List of Tables ....................................................................................................................... iv
Acknowledgement .................................................................................................................v
Dedication ............................................................................................................................ vi
Chapter I: Problem Statement and Significance of the Study
Introduction ................................................................................................... 1
Specific Aims of the Study ........................................................................... 3
References ..................................................................................................... 5
Chapter II: Literature Review of Treatment Decision Making in Cancer
Introduction ....................................................................................................7
Physician Factors Influencing Treatment Decisions in Cancer .....................8
Patient Factors Influencing Treatment Decisions in Cancer ........................ 16
Theoretical Frameworks in Treatment Decision-Making ............................ 23
Decisional Role Preferences ........................................................................25
Priority of Information Needs in Cancer Patients ........................................28
Conclusion ...................................................................................................39
References ....................................................................................................41
Chapter III: Patient and Physician Perspectives on Treatment Decision Making in Older
Adults Newly Diagnosed with Myeloma
Introduction ..................................................................................................55
Treatment Considerations in Older Adults ..................................................57
Purpose .........................................................................................................59
Methods ........................................................................................................59
Results ..........................................................................................................63
Discussion ....................................................................................................84
Conclusion ...................................................................................................91
References ....................................................................................................92
i
Page
Chapter IV: Decisional Role Preferences in Older Adults Newly Diagnosed
with Myeloma
Introduction ................................................................................................. 98
Theoretical Framework ................................................................................99
Purpose .......................................................................................................101
Methods ......................................................................................................101
Results ........................................................................................................105
Discussion ..................................................................................................117
Conclusion .................................................................................................120
References ..................................................................................................122
Chapter V: The Priority of Information Needs in Older Adult Patients Newly
Diagnosed with Myeloma
Introduction ................................................................................................130
Purpose .......................................................................................................131
Methods ......................................................................................................132
Results ........................................................................................................142
Discussion ..................................................................................................151
Conclusion ................................................................................................ 155
References ..................................................................................................156
Appendix A: The Control Preferences Cards ....................................................................161
ii
LIST OF FIGURES
Page
Chapter IV:
Figure Number
1. Distribution of Respondents On and Off ABCDE Metric ............................................ 108
Chapter V:
Figure Number
1. Priority of Information Needs by Rank .........................................................................151
iii
LIST OF TABLES
Page
Chapter II:
Table Number
1. Degner and Beaton’s Pattern of Control ......................................................................... 25
2. Summary of Information Needs Priorities .......................................................................30
Chapter III:
Table Number
1. Sociodemographic Characteristics of Patient Sample .....................................................64
2. Sociodemographic Characteristics of the Physician Sample ...........................................65
3. Major Themes from Patient Participant Interviews ......................................................... 65
4. Major Themes from Physician Participant Interviews .....................................................68
5. Patient-specific Factors Influencing Treatment Decisions ..............................................72
6. Contextual Factors Influencing Treatment Decisions ...................................................... 74
7. Physician-specific Factors Influencing Treatment Decisions ..........................................78
Chapter IV:
Table Number
1. Degner and Beaton’s Patterns of Decision Making ...................................................... 100
2. Sociodemographic Characteristics of the Sample .......................................................... 106
3. Rank Ordering of Competing Scale Models ..................................................................107
4. Distribution of Decisional Role Preferences ..................................................................109
5. Decisional Role Preferences by Age ..............................................................................110
6. Decisional Role Preferences by Education ....................................................................110
7. Decisional Role Preferences by Income ........................................................................110
8. Patient’s Decisional Role Preference, Category, and Description .................................111
Chapter V:
Table Number
1. Nine Information Items in INQ ………………………………………………………. 134
2. Gulliksen & Tukey and Mosteller’s Chi-square for INQ-Myeloma …………………. 138
3. Circular Triads ...............................................................................................................140
4. Kendall’s Coefficient of Agreement ………………………………………………….. 141
5. Sociodemographic Characteristics of the Sample .......................................................... 142
6. Frequencies of Participants’ Preference for 36 Paired Items in the INQ ....................... 144
iv
ACKNOWLEDGEMENTS
This work would not have been possible without the excellent mentorship of Donna L. Berry
(Supervisory Committee Chair) and Barbara B. Cochrane (co-sponsor of my NRSA grant),
and the support of my dissertation committee members Ardith Doorenbos and Karen G.
Schepp. Special thanks to Helene Starks for serving as my Graduate School Representative.
My sincere appreciation also goes out to Jerald R. Herting, Kevin Cain, and Jeff Sloan for
their statistical support. I would also like to acknowledge the recruitment assistance provided
by Aimee Kohn, Lynley Fow, Pamela Becker, Elaine Zedella, Judy Petersen, Kenda Burg,
Seema Singhal, Jose Velasquez, and Tom Blackney.
I also wish to thank the UW School of Nursing Department of Biobehavioral Nursing and
Health Systems for the research training and seminars they provided me throughout my
doctoral studies. This work was generously supported by the National Institutes of HealthNational Institute of Nursing Biobehavioral Training Grant (grant number T32 NR07106 )
and a National Research Service Award Grant (grant number 1 F31 NR011124-01A1), the
Achievement Rewards for College Scientists (ARCS) Fellowship 2006-2009, Seattle chapter,
and the Hester McLaw’s Nursing Scholarship.
v
DEDICATION
This work is dedicated to my partner J. Todd Babcock, who has been a loving companion
throughout the many years of my scholastic journey.
vi
1
CHAPTER I
Problem Statement and Significance of the Study
Introduction
Multiple myeloma is a disease of the elderly, affecting over 20,000 individuals
annually with a median age at the time of diagnosis of 70 years.1 The advent of novel agents
like bortezomib and lenalidomide has increased the number of treatment options for older
patients newly diagnosed with myeloma, but it has also led to clinical uncertainties as to
which of the new therapies might be optimal for a particular patients. These uncertainties are
due in part to the lack of randomized controlled trials comparing the new treatment options
with the existing options. Additionally, older adults have been under-represented in clinical
trials, which has led to a paucity of evidence-based treatment guidelines in this patient
population. All of these factors complicate the decision-making process, supporting the need
for an in-depth examination of various influences impacting treatment decisions.
Research on treatment decision making in the cancer patient population has focused
primarily on the examination of patients’ decisional role preferences.2, 3 Both qualitative and
quantitative findings support the conclusion that older patients want to be informed and
consulted with regard to the impact of various cancer treatments, not only on survival, but
also quality of life.4, 5 With other cancer diagnoses, in which adults have multiple choices,
there is strong evidence that personal factors and the individual preferences of patients play
an major role in how the treatment decision is made.6-11 However, we have very limited
knowledge and understanding of the personal factors that older adults newly diagnosed with
2
multiple myeloma consider when making their treatment decisions. Additionally, we have
limited knowledge of the relevant factors pertaining to treatment decisions that physicians
consider, particularly their valuation of patients’ quality of life during the treatment decisionmaking process.
A better understanding of control preferences over treatment decisions in older adults
newly diagnosed with myeloma could raise physicians and other health care professional’s
sensitivity to patients’ needs. Consequently, addressing patients’ decisional role preferences
could lead to better patient participation and increased satisfaction with care. Improving
decision satisfaction and reducing decisional conflict and decision-related anxiety and
depression are important goals in cancer care.12 However, no data exist regarding how
degree-of-participation influences treatment decisions or various personal factors ultimately
impact decision outcomes in older myeloma patients.
Assessing the priority of information needs in patients with cancer—and then
providing the information needed—are both important tasks for physicians, advanced
practice nurses, and nurses.13 Research has shown that providing the right kind of
information to cancer patients leads to improved satisfaction with decisions, better coping
with the stress of diagnosis, decreased psychological distress, and increased patient
participation during treatment decision-making.14 In this era of scarce health care resources,
providing the right information at the right time is paramount. Nurse interaction with cancer
patients, while essential, is costly, and needs to be more efficient.15 Nurses spend more time
with cancer patients than other health care professionals and are able to assess a patient’s
information needs to prevent a communication breakdown. Older adults newly diagnosed
3
with myeloma need accurate, timely, and appropriate information to help them make
autonomous decisions.
The examination of both patient- and physician-related factors influencing treatment
decisions and the assessment of patients’ decisional role preferences may help clinicians
better understand the decision-making process. Understanding the important factors that
influence a patient’s treatment decision and their decisional role preferences may help
clinicians develop innovative ways to improve a patient’s satisfaction with their treatment
decision. Finally, understanding a patient’s priority information needs can help guide
clinicians in providing valuable patient education.
Study Aims
The overall purpose of this study was to examine treatment decision making in older
adults newly diagnosed with myeloma. This study was conducted with the following specific
aims:
1) Describe physicians’ perspectives on the decision-making process and their patients’
preferences for participation in decision-making.
2) Examine patients’ perspectives on the decision-making process including personal values,
preferences for participation, and past experiences relevant to treatment decision-making.
3) Describe information needs and preferences for participation in decision-making of
patients newly diagnosed with multiple myeloma.
4) Explore the association of sociodemographic variables with information needs priorities
and preferences for participation in decision-making.
4
The remainder of this dissertation is formatted as four chapters, each comprised of a
stand-alone paper. Chapter II is a review of literature and previous research findings relevant
to this study, including a detailed discussion of the relevant physician- and patient-specific
factors, including personal beliefs and values, preferences for participation, contextual
factors, and health-related experiences influencing treatment choices. In Chapter III, the
author describes the evaluation of physicians’ personal perspectives on the decision-making
process and their perceptions of patient preferences for participation in decision making. The
information needs priorities and preferences for participation in decision making in older
adults newly diagnosed with myeloma, along with the association of sociodemographic
variables with information needs priorities and preferences for participation in decision
making are reported in Chapters IV and V. Implications for practice and future research are
presented for each set of findings.
5
References
1.
Jemal A, Siegel R, Xu J, et al. Cancer statistics, 2010. CA Cancer J Clin.
2010;60(5):277-300.
2.
Tariman JD, Berry DL, Cochrane B, et al. Preferred and actual participation roles
during health care decision making in persons with cancer: a systematic review. Ann
Oncol. 2010;21(6):1145-1151.
3.
Singh JA, Sloan JA, Atherton PJ, et al. Preferred roles in treatment decision making
among patients with cancer: a pooled analysis of studies using the Control
Preferences Scale. Am J Manag Care. 2011;16(9):688-696.
4.
Gaston CM, Mitchell G. Information giving and decision-making in patients with
advanced cancer: a systematic review. Soc Sci Med. 2005;61(10):2252-2264.
5.
Chouliara Z, Kearney N, Stott D, et al. Perceptions of older people with cancer of
information, decision making and treatment: a systematic review of selected
literature. Ann Oncol. 2004;15(11):1596-1602.
6.
Berry DL, Ellis WJ, Woods NF, et al. Treatment decision-making by men with
localized prostate cancer: the influence of personal factors. Urol Oncol.
2003;21(2):93-100.
7.
Zeliadt SB, Moinpour CM, Blough DK, et al. Preliminary treatment considerations
among men with newly diagnosed prostate cancer. Am J Manag Care.
2010;16(5):e121-130.
8.
Knight SJ, Latini DM. Sexual side effects and prostate cancer treatment decisions:
patient information needs and preferences. Cancer J. 2009;15(1):41-44.
6
9.
Hawley ST, Lantz PM, Janz NK, et al. Factors associated with patient involvement in
surgical treatment decision making for breast cancer. Patient Educ Couns.
2007;65(3):387-395.
10.
Denberg TD, Melhado TV, Steiner JF. Patient treatment preferences in localized
prostate carcinoma: the influence of emotion, misconception, and anecdote. Cancer.
2006;107(3):620-630.
11.
Lee CN, Hultman CS, Sepucha K. What are patients' goals and concerns about breast
reconstruction after mastectomy? Ann Plast Surg. 2010;64(5):567-569.
12.
Weinfurt KP. Outcomes research related to patient decision making in oncology. Clin
Ther. 2003;25(2):671-683.
13.
Rutten LJ, Arora NK, Bakos AD, et al. Information needs and sources of information
among cancer patients: a systematic review of research (1980-2003). Patient Educ
Couns. 2005;57(3):250-261.
14.
Husson O, Mols F, van de Poll-Franse LV. The relation between information
provision and health-related quality of life, anxiety and depression among cancer
survivors: a systematic review. Ann Oncol. 2010. Epub ahead of print.
15.
Davison BJ, Goldenberg SL, Wiens KP, et al. Comparing a generic and
individualized information decision support intervention for men newly diagnosed
with localized prostate cancer. Cancer Nurs. 2007;30(5):E7-E15.
7
CHAPTER II
A Literature Review of Treatment Decision Making in Cancer
Introduction
Treatment decision making is an important component of cancer care continuum. In
older adults newly diagnosed with myeloma, various treatment options have many tradeoffs
in terms of risks and benefits, ultimately impacting the patient’s quality of life.
Nursing research on cancer treatment decision making (TDM) has been increasing
over the past three decades. Several decision-making studies have focused on answering
questions related to patients’ preferences for participation and information needs,1-6 which
subsequently led to the development of intervention studies aimed at improving patient
education and participation.7,8 A systematic review on information-giving and patient
participation in decision making in patients with advanced cancer revealed that patients have
unmet information needs and widespread misunderstanding of the disease, including its
prognosis and treatment.1 In terms of preferences for participation, there is a huge variation
in preferences, but more than half of patients want some control of the decision
(collaborative and active roles).1, 3, 9 There is also discordance between patients’ preferred
and actual level of participation, with some patients wanting more participation than what
was actualized.3, 9 Overall, decision-making studies’ goals are directed toward achieving
better patient outcomes, which include better compliance with treatment, increased patient
satisfaction, reduced decisional conflicts, and decreased stress and anxiety from TDM.10
In the past, decision research has focused mainly on the physician, which was thought
to be related to the dominant paternalistic model of the patient-physician relationship (where
the physician makes the decision for the patient), commonly seen in practice from the 1960s
8
through 1980s.11-13 However, a shift in the physician-patient relationship paradigm from
paternalistic to shared,14, 15 or more patient-controlled treatment decisions, is becoming more
common in breast16, 17 and prostate18, 19 cancer patient populations. Consequently, decision
researchers have also directed their focus more toward the examination of patient
perspectives on medical decision making.17, 20, 21 For example, Berry and colleagues have
explored the physician perceptions of personal and medical factors22 and the relevant aspects
of treatment decision making reported by men with localized prostate cancer (LPC).23 Both
patient and physician factors related to adjuvant chemotherapy use have also been examined
in older women with breast cancer (≥65 years of age).24
This review highlights the relevant physician and patient factors that could influence
treatment decisions in cancer patients. It also includes a brief review of the different models
or frameworks of decision making and the decisional role preferences and priority of
information needs among cancer patients. Moreover, this review examines the association of
sociodemographic factors with patient’s decisional role participation and priorities of
information needs. Finally, the current trend in patient participation during decision making
is examined and implications for practice and research are suggested.
Physician-related Factors Influencing Decisions in Cancer Treatment
The physician-related factors influencing cancer treatment decisions include the
physicians’ beliefs, attitudes, and values, medical expertise and practice type, medical and
clinical factors, communication style, and the medical literature.
Beliefs, Attitudes, and Values
Physicians’ stated or implied beliefs, attitudes and values are relevant to cancer
treatment decision making. Despite the fact that health-related quality of life (HRQOL) has
9
been reported as an important aspect of cancer treatment outcomes, particularly in older
adults,25 there is evidence that clinical research trials in myeloma have not incorporated
HRQOL as a study outcome.26 The authors of the 13 of 15 RCTs included in that review
stated that the given HRQOL result should influence clinical decision making. However,
HRQOL studies have had limited impact on the published guidelines for developing
supportive care and treatment plans for patients with myeloma, probably because of
weaknesses in study design and measures, plus differing interpretations of the results.26
Perhaps there is also a lower level of enthusiasm among investigators for HRQOL outcomes
in myeloma clinical trials.
Bouchardy and colleagues27 conducted a study which reviewed treatment outcomes in
older women with breast and ovarian cancers and reported that the physician’s belief about
who maintains control of the decision could play a role in the undertreatment of cancer.
Undertreated women were found to have a significantly poorer prognosis, yet none of the
reviewed studies examined factors that could lead to undertreatment. The authors did cite
some potential reasons for undertreatment, which included the physician misconceptions and
beliefs about diminishing benefits of treatment and beliefs that more harm than good will
come from treatment. A physician’s belief that he or she should dominate the TDM process,
plus an intentional disregard of full disclosure, were also suggested as among the potential
reasons.
In an ethnographic study involving 25 women with breast cancer, Freedman28
reported that the physician’s power to choose what is told to the patient and what is withheld
is a powerful determinant in the medical decision-making process. Although this finding was
supported in the report by only two illustrative cases, it underscores the possibility that when
10
a physician believes the patient is simply a passive recipient of care, the physician’s preferred
treatment could trump the patient’s preference and influence treatment decisions. For
example, in a study of 21 women with ovarian cancer, ages 47-77 (mean age, 60.6 years), the
majority of participants (66%) perceived that the physician largely directed the interaction
during the medical encounter and there were really no treatment choices offered and that they
were simply told to have the therapy that the physician prescribed.29 Men with newly
diagnosed LPC have also reported that the decision-making process was provider-led and
they were passive recipients of decisions made by others. Cohen and Britten30 interviewed 19
men with LPC between the ages of 58 and 88 (mean age 74.4) using a semi-structured
interview and found that patients perceived their treatment plans were mostly decided by
their clinicians. These reports are surprising since they are contrary to a more recent study,
which showed that 91.7% (N=133) of 145 men with newly diagnosed LPC were able to
actualize their preferred decisional role participation.19 Perhaps this can be partially
explained by the growing health care consumerism in North America.
Some oncologists place a higher value on improvements in a patient’s overall
survival, as compared to their patients, who place a higher value on quality of life (QOL). A
study on decision making and QOL in older patients ages 60-85 years with acute myeloid
leukemia or advanced myelodysplastic syndrome revealed that the physician’s opinion was
influential in older adults’ decisions to undergo intensive chemotherapy. This study showed a
physician preference for intensive chemotherapy, since this treatment is typically associated
with improved overall survival rates. Unfortunately, as the chemotherapy treatment gets
more intensive, it poses significant morbidity risks, which in turn affect the patient’s overall
11
QOL. Ninety seven percent (N=42) of 43 participants agreed with the statement that quality
of life was more important to them than quantity of life.31
Ravdin, Siminoff and Harvey32 surveyed members of the National Alliance of Breast
Cancer Organizations (NABCO), and 562 individual members responded. When asked what
lowest degree of absolute benefit they found acceptable for a treatment option, the
respondents’ median acceptable extension of life expectancy was 3 to 6 months, and the
lowest acceptable reduction in recurrence risk was 0.5 to 1.0%. The researchers also noted
that there was a considerable variation in outcomes expectations, with 27% of participants
not considering anything less than 1 year of life expectancy as acceptable and 26% not
accepting less than 5% reduction in recurrence risk as a worthwhile trade-off for the reduced
HRQOL that inevitably accompanies treatment. Many women could not recall hearing
quantitative survival information. Since treatment has an impact on patient’s QOL, the
researchers suggested that physicians should give patients quantitative estimates of overall
survival instead of qualitative descriptions in order to fully inform patients about survival
benefits of the therapy.32
Collaboration between oncologists and primary care physicians (PCP) is important in
the context of complex medical conditions, especially those seen in older adults with multiple
comorbidities and advanced cancer. A collaborative relationship between oncologists and
PCPs is vital since a patient’s PCP could be the patient’s primary source of health
information relating to treatment decisions. A recent study33 showed that oncologists have
various perspectives on how involved PCPs should be in terms of treatment and procedurerelated decisions in older adults with cancer. Fourteen percent of the oncologists surveyed
believed that PCPs should be more involved with decisions. Additionally, the study showed
12
variability in oncologists’ reports on the frequency of their communication with PCPs about
treatment goals or TDM. This variability indicates that oncologists may have very different
preferences for PCPs’ participation in treatment or procedure-related decision making or too
busy to communicate with PCPs, a factor that could potentially influence the decision and
ultimately affect the patient’s satisfaction with that decision.33
Medical Expertise and Specialty Type
The physician’s medical expertise and specialty type have been shown to influence
treatment decisions as well. A recent survey of American Society of Clinical Oncology
(ASCO) members on the management of sentinel node breast micrometastases (SNMM)
revealed that radiation oncologists (76%) were more likely than medical oncologists (57%)
or surgeons (47%) to consider axillary radiation instead of axillary dissection for SNMM (P
= 0.0021). These findings are inconsistent with ASCO’s guidelines, which recommend
axillary dissection as the primary treatment for SNMM in patients diagnosed with breast
cancer.34
In another survey of physicians,35 Hodgkin’s lymphoma specialists were more likely
to tailor therapy according to individual patient factors, while decisions of non-specialist
physicians were influenced more by the number of Hodgkin’s lymphoma cases they have had
in their practice. These findings were based on survey responses from 81 Hodgkin’s
specialists and 73 randomly selected physicians from the American Society for Radiation
Oncology (ASTRO) and ASCO membership lists. Moreover, physicians working in teaching
institutions were more likely to choose combined modality therapy (CMT) over radiation
therapy or chemotherapy alone.35 Although the overall survey response rate was only 50%
(58% among Hodgkin’s lymphoma specialists and 43% for randomly selected oncologists), it
13
is important to note that 92% of the Hodgkin’s lymphoma specialists who responded were in
teaching institutions, settings that typically follow established treatment guidelines such as
CMT for Hodgkin’s disease. However, this study must be interpreted with caution, since
there were limited treatment choices provided to the respondents and individual contextual
factors were not accounted for during analysis.
In patients with LPCs, a national survey documented that urologists tended to favor
surgery, while radiation oncologists tended to favor radiation therapy over surgery.36 In an
international survey, gastroenterologists favored surgery for the management of gastric
lymphoma, while hematologists and oncologists were inclined to favor more conservative
therapies.37 Overall, these studies support the notion that the physician’s expertise and type
of practice influence treatment choices.
Effects of a Lowered Life Expectancy with Increased Age on Treatment Offered
Life expectancy and quality of life are two major factors in TDM, especially in
patients diagnosed with cancer.38 The physician’s perception of a shorter-than-normal life
expectancy has led to decreased adjuvant chemotherapy use among older adults diagnosed
with stage III colon, breast, and non-small-cell lung cancers.39, 40 In two retrospective cohort
studies using the Surveillance, Epidemiology, and End Results/Medicare-linked database,
researchers found that physicians used implicit judgments about age and lowered life
expectancy to decide whether or not they would utilize adjuvant chemotherapy after surgery
for stage III colon cancer39 and breast cancer.41 Schrag et al.39 reviewed the records of 6,262
Medicare beneficiaries diagnosed with stage III colon cancer from 1991 through 1996. Study
participants included mostly Caucasians ages 65-90 (mean age not reported), 24% of whom
were at the bottom quartile of the median household income for their census track of
14
residence. The researchers found that the use of adjuvant chemotherapy after surgery
declined dramatically with increasing chronologic patient age, after adjustment for potential
confounders such as comorbidities. Among 3,391 patients with no comorbidities, the use of
adjuvant chemotherapy was given in 80% of patients ages 65-69 years, 64% of whom were
75-79 years and 13% 85-89 years. These findings are consistent with a prospective study
which revealed that a smaller proportion of patients with colorectal cancer above the age of
75 received surgery with chemotherapy as compared to those younger than 75.42 The
researchers in that study suggested that the older patients should have received adjuvant
therapy, since patients in their 70s and 80s continue to have a reasonably long life expectancy
and arguably with good QOL. Overall, the study demonstrated that physicians’ assumptions
of a lower life expectancy based exclusively on age may explain this low utilization of
adjuvant chemotherapy among the elderly with colon cancer.
A retrospective study examining the factors that influenced treatment decisions in
older breast cancer patients at a single center found similar underutilization of treatments in
this patient population. Hurria et al.41 reviewed records of 216 Memorial Sloan-Kettering
Cancer Center (MSKCC) patients stratified into two age groups: 75-79 years and ≥80 years.
These researchers found treatment differences in women with breast cancer ages 75-79 as
compared to patients ages 80 and above. Patients in the older age group were less likely to
receive an axillary lymph node dissection and radiation therapy. There were no data included
in the report on non-medical factors that might influence a patient’s preference for not
receiving therapy. The study revealed that the physician’s assumption of a lowered life
expectancy in older adults impacted treatment decisions.
Medical and Clinical Factors
15
Tumor type, cytogenetic profile, age-related physiologic decline, and the presence of
other comorbidities influence physicians’ treatment decisions.21, 43 For example,
chemotherapy is of greatest value in older adults with node-positive, estrogen receptornegative and progesterone receptor-negative breast cancer.40, 44 Chemotherapy decisions for
older cancer patients generally involve adjustment of the dose based on the patient’s renal
function, prophylactic use of growth factors, maintenance of hemoglobin levels around 12
g/dL and proper drug selection based on age-related pharmacokinetics.45
Communication Style
Physicians’ communication patterns could inhibit older patients’ communication
about their own treatment preference, potentially causing post-decisional dissatisfaction. A
recent study46 of older patients with early-stage breast cancer showed that oncologists were
significantly more verbal and direct with older adult patients than with middle-aged patients,
in terms of expressing their treatment preference. According to the researchers, oncologists in
the study sample appeared to change their communication with older patients to engage in
smoother, slower, and more directive manner. The researchers suggested that these actions
may be traced to traditional age stereotyping and recommended that oncologists should not
be overly directive in their style of communication, but instead allow for patient participation
during treatment consultations.
Medical Literature
Aside from comorbid conditions that are included as medical factors influencing
treatment decision, physicians have also ranked the medical literature as an important factor
they consider.21 In contrast, patients ranked social issues such as family preference and
16
family burden during therapy as more important. When physicians make evidence-based
decisions, they are using the medical literature to support their treatment decisions.47
Patient-related Factors Influencing Decisions in Cancer Treatment
The patient-related factors influencing cancer treatment decisions include the
patients’ beliefs and values, ethnicity, decisional role preferences, health-related experience,
perception of the decision making process, personal, and contextual factors.
Beliefs and Values
The patient’s personal beliefs and values have been shown to influence treatment
decision-making. One study prioritized patients’ rankings of physician’s opinion, family
preference, and family burden as important, respectively.21 These findings were corroborated
by another study, in which older adults made their treatment decisions depending on the
burden of the treatment, possible outcomes, and likelihood of adverse functional and
cognitive outcomes.48 Using a structured questionnaire, Fried et al.48 surveyed the treatment
preferences of 200 individuals who were 60 years of age or older (mean age 72.8) and had a
limited life expectancy due to cancer, congestive heart failure, or chronic obstructive disease.
Of these 200 patients, 79 had cancer (mean age 71.7) and were interviewed at home about
treatment preferences according to three components of therapy: the burden it imposed, the
possible outcomes, and the relative likelihood of each of these outcomes. The researchers
found that when the possible outcome was survival with severe functional or cognitive
impairment, respondents no longer wanted the therapy. This study illustrated that older
patients’ treatment preferences may change in response to the perceived burden of therapy,
quality of outcomes, and probability of achieving the desired outcomes.
17
In patients diagnosed with prostate cancer, a recent review has found that they put
more value on survival than sexual function; however, in terms of the range of possible side
effects from treatment, patients put more value on sexual function than other side effects.49
According to the authors of that review, men with newly diagnosed prostate cancer may have
overestimated the risk of sexual problems associated with treatment for prostate cancer and
have unmet information needs related to prostate cancer treatments. One could infer that the
value patients place on certain HRQOL components may change depending on their
understanding of available information.
Quality of life has always been an important value for older adults, who have ranked
it consistently at the top of their priorities.50-55 For example, among older adults, median age
72 years, with acute myeloid leukemia or advanced myelodysplastic syndrome, 97% (N=42)
of 43 participants agreed QOL was more important than length of life, regardless of their
therapy choice.31 Older adults (76-91 years old, mean age 83.4) with breast cancer (N=21) at
various stages also ranked independence as a high-priority value.54
Ethnicity
Although ethnic or national stereotypes are subjective and not universal, there are
some documented commonalities among groups that are informative when considering
factors influencing cancer treatment decisions. In a recent study of 329 Greek women
diagnosed with breast cancer, researchers found that a great majority of patients (71.1%)
preferred a passive role in decision making., This finding is in contrast to a lower proportion
of passive preferences reported in similar breast cancer studies from North American and
Western European countries.56 Another study reported that Korean Americans and Mexican
Americans were more likely that non-Hispanic Caucasians to believe that the family should
18
make decisions about the use of life support.57 These first two groups are therefore more
likely have a family-centered approach to decision making.57 According to this study,
Caucasians valued individualistic beliefs (self-reliance, self-responsibility, and control) and
were thus likely to hold to a “shared” model of decision-making57 while Hispanic and
African American participants were more likely to value collectivism and to hold to a familycentered or paternalistic approach to decision making.58 These findings must be interpreted
with caution because they are based on small studies with participants from specific
geographical locations (in one case urban Southern California),57 which may differ
significantly from other locations in both ethnic distribution and attitudes. A more recent
review paper also concluded that Asians, particularly Koreans, preferred to share their
decision making on end-of-life care with their families.59
Decisional Control (Role) Preferences
Older adults often express a desire for shared decision-making, but the variation in
their desire to participate in the process is substantial.1, 60-62 One recent study of 73 patients
ages 70 to 89 years (mean age, 76) diagnosed with metastatic colorectal cancer reported that
23% of these older adult patients preferred a collaborative role, 25% an active role and 52% a
passive role.60 Other studies have shown that older and less-educated individuals were most
likely to prefer passive roles60, 63 while younger, more educated women were most likely to
prefer participatory decision making.1, 64-66 Gender may have been a factor in decision
making roles according to a study in Britain of older men (58-88 years old, mean 74)
diagnosed with prostate cancer, in which the men took a passive role during a treatment
discussion but later wanted to revisit the decision-making process.30 Role preferences can
also change with time. One study uncovered the dynamic nature of role preferences by
19
showing that cancer patients’ preference for involvement declined as they became sicker.67
Because patients’ preferences for participation in decision making are dynamic, follow-up
assessments of patients’ decisional role preferences are important.
A recent study of breast cancer patients showed that regardless of race and ethnicity,
a higher (more patient-based) rather than lower (more surgeon-based) level of patient
participation during decision making was associated with a patient’s decision to have a
mastectomy.68 The sample included 23.9% Latina (12.0% low acculturated, 11.9% high
acculturated), 27.1% African American, and 48.9% white. In Greece, most women diagnosed
with breast cancer (71.1%) preferred a passive role and were found to request less frequent
mammography (p<0.001) and/or Papanicolaou tests (p<0.0005) pre-diagnostically.56 These
studies demonstrate how decisional control preferences vary and can be associated with
actual treatment or health care screening decisions.
Health-Related Experience
Previous health-related experiences or familiarity with treatment options can
influence treatment choice.69, 70 Using grounded theory methods, Berry and colleagues23
uncovered a set of related, meaningful, and influential factors among men who had made
recent decisions for localized prostate cancer. Past experience with cancer was the context in
which the men in the study deliberated about the current choices. Moreover, the participants
were making decisions based on their perceived “best choice” to treat the prostate cancer. A
study examining factors that affect older women’s decisions to have regular screening
mammograms found that previous negative experience with mammography had considerable
influence on their decisions not to continue with a schedule of recommended regular
screenings.71 Similarly, a previous negative experience from a colorectal screening procedure
20
(e.g., fecal occult blood or colonoscopy) has been reported as a factor for study participants
deciding not to get up-to-date colorectal cancer screenings.72
Perception of the Decision-Making Process
The patient’s positive or negative perception of the decision making process could
potentially influence cancer treatment decisions. Using content analysis, researchers73
conducted a study exploring the conditions for participation and non-participation in decision
making in 362 patients of all diagnoses and conditions in a Swedish medical center. The
authors reported that patients participate in health care when information is provided based
on their individual needs, when they receive the knowledge needed, and when decisions are
made based on their knowledge and needs.
Older women diagnosed with breast cancer have also reported higher participation in
decision making when they have ample time to exchange information and when their family
members are included in the decision-making process. Kreling et al.74 conducted a focus
group interview with 34 older women with breast cancer who were from different ethnic
backgrounds (29% Black, 53% White, and 18% Latina) to explore the barriers and promoters
of chemotherapy use. The researchers noted that there was typically less than adequate time
for patient-physician communication prior to decision making—particularly when women of
color were the patients—which resulted in a less-than-optimal use of chemotherapy.
An evaluation of the patient’s perception of the decision making process would be
helpful to health care professionals in promoting future patient participation during decision
making. Clearly, according to the research, attention to the patient’s information needs and
adequate communication exchange between the provider and the patient are important
considerations in increasing patient participation in health care.73, 74
21
Personal Factors
Personal factors that could influence cancer treatment decisions include selfdescription, potential treatment outcomes, past experience with cancer, and influential people
in the cancer patient’s lives, such as a physician they visited before or other men who shared
their beliefs, perspectives, or characteristics.23 Berry and colleagues23 were the first to
include a systemic description of “Who I am and what I do” and “Making the best choice for
me” as influential aspects of decision making among men with LPC. These descriptive data
were obtained from focus groups and individual interviews of 44 men (mean age, 64.8 years)
who were within 6 months from the time of diagnosis. The study was exploratory in nature
and only 16% of participants were men of color. Denberg and colleagues75 also reported that
emotions, misconceptions, and anecdotes influence treatment preferences in patients with
prostate cancer. Using semi-structured interviews, Denberg and colleagues explored the
personal beliefs and attitudes of 20 men with clinically-localized prostate cancer following
their first consultation with a urologist, but before treatments were initiated. Using grounded
theory methods, the researchers analyzed the patients’ personal views about prostate cancer
and treatment options, emotional reactions to the diagnosis, treatment preferences,
information sources, and perceptions of interactions. The researchers found that patients felt
the treatment decision-making was made in the setting of intense emotion, a context in which
patients were also dealing with fear and uncertainty and had a sense of urgency and needing
to make a decision right away.75 The researchers discovered that patients had misunderstood
prostatectomy, risks and benefits of other treatment options, and the importance of a second
opinion. They also found that patients compared themselves to other men they knew who had
been treated for prostate cancer to justify their own treatment decisions. The researchers
22
concluded that patients’ personal factors had an important influence on treatment choice in
men with LPC.
Patient Contextual Factors
The availability of a caregiver or a family member influences treatment decisions 74
and to some extent could lead to decisional conflicts among family members.76, 77 In a crosssectional survey study that achieved a 64% response rate, patients with colorectal cancer ages
65-92 (mean age 75.8, n=67) ranked family preference, family burden, and traveling for
treatment, respectively, as the most important factors influencing their treatment decisions.21
Lack of insurance, poor financial status, and geographical barriers have been also reported as
important contextual factors that can influence treatment choices.39, 42, 78 Costs, social
support, and logistics (such as transportation and lodging) were reported as key issues in
treatment selection for adjuvant therapy in older adults with breast, colon, and non-small-cell
lung cancers.40
In a study examining the factors that influenced the use of chemotherapy in 34 older
adults (≥65 years of age, age range and mean were not reported), Kreling and colleagues74
have identified patient contexts influencing therapy decisions. These contextual factors
include secrecy, age, and age-related themes such as functional status and life expectancy,
and perceived benefits and side effects of chemotherapy. Depending on the individual
combinations of contextual factors for a specific participant, these contextual factors were
found to facilitate or inhibit the use of chemotherapy in this group of ethnically diverse older
women with breast cancer (18 Caucasians, 10 African-Americans, and 6 Latinas).74
23
Theoretical Frameworks for Patterns of Decisional Role Preferences
Physician-based, Shared, and Patient-based TDM
There are several frameworks or models of decision making that have been proposed
by decision researchers. In a cross-sectional interview study of 467 consecutive patients seen
in a general medicine clinic, Mazur and Hickam found that 68% preferred shared decision
making, 21.4% preferred physician-based decision making and 10.5% preferred patientbased decision making.79 This study corroborates the decision-making roles described earlier
by Degner and Beaton.80
Non-deliberative Delegators, Deliberative Delegators, Non-deliberative Autonomists, and
Deliberative Autonomists
A typology of decisional roles has been suggested based on a sample of 5,199 older
adults from the Wisconsin Longitudinal Study. Flynn and colleagues81 described four
prevalent types of role preferences, which include non-deliberative delegators (23%),
deliberative delegators (16%), non-deliberative autonomists (11%), and deliberative
autonomists (46%). Non-deliberative delegators wanted the physician to make the decision
and typically did little deliberation on their own, while deliberative delegators wanted the
physician to make the decision but also did a lot of deliberation. Non-deliberative
autonomists wanted control over decisions but did little deliberation, while deliberative
autonomists wanted personal control and did a lot of deliberation.81
Patterns of Decision Making
A well known model of decision making was developed by Degner and Beaton in
1987. “Patterns of Decision Making”80 has been widely used as a theoretical framework for
decisional role studies. The framework encompasses the various levels of patient
24
participation in decision making and is patient-centered, directly eliciting participation
preferences from the patients’ perspectives. According to the model, four decision-making
patterns may occur. A provider-controlled decision-making pattern emerges when patients
refuse to become involved in selecting their own treatment, even when urged to do so by the
physician. These patients are essentially saying: It’s up to you, Doctor; you’re the expert. On
the other hand, a patient-controlled decision-making pattern occurs when patients want to
make their own treatment choices.80 When patients indicate a need to discuss available
options with their physician and ask for time to think and discuss options prior to making the
final decision with the physician at the next visit, a jointly-controlled decision-making
pattern occurs. Finally, when patients are incapacitated and unable to make the treatment
decision, the family makes treatment decisions for them, resulting in the family-controlled
decision-making pattern.80
The description of the patterns of decision making mentioned above provided the
initial parameters for the Control Preferences Scale (CPS).82 Table 1 illustrates the various
roles patients can play during treatment decision-making. This framework of decisional role
patterns was developed based on a 4-year qualitative study into decision-making roles in lifethreatening situations such as cancer.80 The CPS, a measure of decision-making preferences
developed by Degner and colleagues,82 identifies these preferences on a series of cards and
were utilized to elicit patient preferences for participation during TDM in this study.
25
Table 1. Degner and Beaton’s Pattern of Control (The Control Preferences Scale)80
Active – Patient-Controlled
o I prefer to make the final treatment decision (Card A).
o I prefer to make the final treatment decision after seriously considering my doctor’s
opinion (Card B).
Collaborative – Jointly-controlled
o I prefer that my doctor and I share responsibility for deciding which treatment is best
(Card C).
Passive – Provider-controlled
o I prefer that my doctor makes the final treatment decision but that he seriously
considers my opinion (Card D).
o I prefer to leave all treatment decisions to my doctor (Card E).
Family-Controlled
o Family makes final decisions because the patient is incapacitated (no card)
Decisional Role Preferences
The patients’ decisional role preferences have been the subject of inquiry among
decision making researchers in the past three decades. Initially, researchers have examined
the patient’s preferred role, but subsequently they have examined both the preferred and
actual role during decision making.3, 9
Preferred and Actual Role in Cancer-related Decision Making
Frameworks or models of decision making have emphasized the various roles patients
prefer or actualize during the decision making process. Consequently, decision researchers
have examined the degree of control patients wanted and achieved during decision making
and explored the impact of various preferences for level of participation to decision outcomes
such as satisfaction and psychological well-being. A recent systematic review of patient’s
decisional preferences in cancer-related decision making has found that patients wanted a
more shared or active role versus a less passive role.3 Twenty-two studies involving patients
with breast, prostate, colorectal, lung, gynecological, and other cancers were included in the
26
review. The authors concluded that patients wanted more participation than what actually
occurred during the decision-making process.3 Additionally, this review validated findings
from an earlier systematic review on the same topic, which reported that patients with cancer
have large variations in decisional role preferences.1 Interestingly, patients with colorectal
cancer were found to have the lowest percentage of active role preference (6%) while
patients with prostate cancer (86%) and patients with breast cancer (97%) reported the
highest percentage of shared and active role preferences, higher than those with colorectal,
gynecological or lung cancer.3
A recent meta-analysis9 of 3491 participants from U.S. and Canadian clinical trials
(26% and 74% of the sample, respectively) reported similar findings, that patients wanted
more control than what was actualized. Although 49% of the participants preferred a
collaborative role, only 34% achieved this role; 25% wanted a passive role but only 36%
actually experienced this passive role, resulting in an overall discordance between preferred
and actualized roles of 39% (n=1058). Based on the findings of this meta-analysis, Singh and
colleagues9 recommended that clinicians assess and individualize their communication
approach to cancer patients.
Demographic Factors Associated with Decisional Control Preferences in Cancer Patients
Sociodemographic factors associated with decisional role preferences may include
age, gender, education, ethnicity, employment status, and partner status. In some studies, two
or more factors have been associated with specific decisional role preference.
Patients younger than 50 years were found in many studies to be significantly more
likely to prefer active or collaborative roles during treatment decision making.16, 64, 66, 83-87
Similarly, older adults with cancer were found to prefer a passive role.9, 63, 88-91 Other studies
27
have reported no association between age and role preferences.65, 92-96 Based on these reports,
age alone may not be a strong predictor of role preferences, but might be moderated by other
sociodemographic factors that influence decisional role preferences.
Gender has been reported as a factor influencing decisional role preference. Being
male was found to be associated with a passive role preference in two studies.88, 91 A recent
meta-analysis reported that more women than men experienced a passive role.9
Education has also been reported as a factor known to be associated with decisional
role preferences. Several studies have reported that higher education was found to be
associated with a preference for more patient control during decision making in several
studies.17, 64, 85, 87, 88 However, other studies have not found an association between
educational level and TDM role preferences.92, 95, 96 The small sample sizes (N= ≤150) of
these latter studies may have limited more meaningful association analyses.
In terms of employment or partner status and role preferences, one study reported that
being employed is associated with active role preference,85 while another study reported that
married men and women were more likely to prefer a passive role.86
Ethnicity may also be a factor associated with decisional role preferences. East
Asians,97, 98 Hispanics, and African Americans were found to be more likely than Caucasian
to prefer passive or collaborative than active roles in TDM.57 Two studies have reported
specifically that they found no association between various sociodemographic variables and
TDM role preferences.60, 99 More studies are needed to better understand the relationship
between ethnicity and role preferences.
As discussed above, several studies have shown that certain sociodemographic factors
are associated with decisional control preferences in cancer patient populations. However,
28
there is a high variability in patients’ decisional role preferences, and sociodemographic
factors only account for a small percentage of the variance in role preferences. Thus, these
associations must be interpreted with great caution, and an individualized assessment of
patient preferences should be investigated across the continuum of care.
Priority of Information Needs in Cancer Patients
Information seeking about the disease, its prognosis and treatment remains a major
area of need for individuals with cancer.100 In order to truly help patients make autonomous
decisions, health care professionals have a responsibility to provide accurate, timely, and
meaningful information to patients. Since health care resources are limited, prioritizing
patients’ information needs is an important step towards efficiency.
Oncology nurses play a critical role in providing individual information to patients
with cancer. The measurement and prioritization of the patient’s information needs in nursing
care potentially offers the following advantages: 66, 89, 101

directs the attention of nurses to the highest information needs

guides nurses to prioritize patient teaching and information giving

saves time and enhances the quality of information that patients will receive

provides relevant information to patients at specific points of their disease and
recovery trajectory

makes nurse-patient encounters more meaningful

lowers the psychological distress associated with treatment decision-making, and

helps patients assume a more active role in decision making generally.
Table 2 outlines a summary of studies regarding information priorities of patients
with cancer. Despite recent efforts to prioritize patients’ information needs, the assessment of
29
patients’ priorities for these needs and mapping them over time remain a logistical issue for
most health care professionals.
30
Table 2. Summary Table of Information Needs Priorities in Cancer Patients
Reference
Cassileth et al.102
Sample size, age
range, mean age
256 patients, age
range not reported,
55.5 years
Davison et al.103
74 patients with
their partners, 4079 years, 62.2 years
Wallberg et al.87
201 patients, age
range and mean not
reported; 44% <50
years, 36% 51–65
years, 20% ≥66
years
150 patients; 32-84
years, 54.8 years
Luker et al.104
Methods: design, sample, sampling,
setting; data collection
Cross-sectional, patients with various
types of cancer diagnosed within the past
10 months, consecutive sampling from
hematology/oncology, radiation therapy,
and inpatient oncology unit; survey
investigator-developed ISQ
Quasi-experimental, one-group,
pretest/post-test, patients with prostate
cancer who were diagnosed recently and
had their initial treatment consultation,
convenience sampling from an outpatient
prostate center; survey using the INQ
Cross-sectional, women with breast
cancer diagnosed in the past 18 months,
consecutive sampling from an outpatient
breast cancer clinic; structured interview
using the INQ
Cross-sectional, newly diagnosed breast
cancer patients with an average 2.5 weeks
since diagnosis, consecutive sampling
from a consultant’s list in a large teaching
hospital; structured interview; INQ
Top three patient priorities for
information related to TDM
1. Side effects of therapy
2. Goals of therapy
3. Disease
1. Likelihood of cure
2. Stage of disease
3. Side effects of therapy
1. Likelihood of cure
2. Stage of disease
3. Risks and benefits of various
treatment
1. Likelihood of cure
2. Stage of disease
3. Risks and benefits of various
treatments
30
31
Table 2 continued
Reference
Beaver et al.99
Stewart et al.93
Sample size, age
range, mean age
42 patients, 43-83
years, 66.6 years
105 patients, 21-87
years, 55.8 years
Methods: design, sample, sampling,
setting; data collection
Cross-sectional, primarily male patients
with known first-time colorectal cancer,
convenience sampling from general
practitioner referrals and a small number
of inpatient referrals at a large university
teaching hospital; interview schedule;
INQ
Cross-sectional, women with ovarian
cancer, including those in remission, with
metastasis or distant metastasis,
consecutive sampling from a university
general hospital and at a comprehensive
cancer center; self-report questionnaire;
investigator-developed 5-point Likert
type, investigator-developed 43-item INQ
Top three patient priorities for
information related to TDM
1. Likelihood of cure
2. Stage of disease
3. Risks and benefits of various
treatments
1. Status and nature of the disease
2. Various treatment and success rates
3. Self-care and empowerment issues
31
32
Table 2 continued
Reference
Vogel et al.105
Sample size, age
range, mean age
135 patients, 19-75
years, 53.9 years
Methods: design, sample, sampling,
setting; data collection
Longitudinal, women with newly
diagnosed breast cancer, consecutive
sampling from two breast cancer centers;
mailed questionnaires; investigatordeveloped 5-point Likert-type information
needs questionnaire with eight items
Top three patient priorities for
information related to TDM
At baseline:
1. Treatment
2. Diagnosis
3. Prognosis/medication side
effects/aftercare (equal scores)
At 3 months:
1. Treatment
2. Medication and side effects
3. Aftercare
At 6 months:
1. Aftercare
2. Treatment
3. Prognosis/medication side
effects/examination & medical
tests (equal scores)
32
33
Table 2 continued
Reference
Butow et al.67
Sample size, age
range, mean age
80 patients, 18-87
years, 51 years
Methods: design, sample, sampling,
Top three patient priorities for
setting; data collection
information related to TDM
Longitudinal, patients with various types
Pre-consultation:
of newly diagnosed cancer comprising
1. Information on what is happening
mostly women (75%), consecutive
to the cancer
sampling from two participating
2. Information on the likely future of
oncologists at a university hospital; survey
the illness
questionnaires using ISQ
3. Information about the illness
Immediately after consultation:
1. Information on support in the form
of reassurance that they would be
“looked after”
2. Information on support in terms of
reassurance and hope
3. Information on support in terms of
a chance to talk about worries and
fears
3-6 months after first visit (N=40)
1. Information on the likely future of
the cancer
2. Information on what is happening
to the cancer
3. Information on support in terms of
reassurance and hope
33
34
Table 2 continued
Reference
Bilodeau &
Degner89
Sample size, age
range, mean age
74 patients, 18-83
years, 57.5 years
Davison, Degner,
& Morgan106
57 patients, age
range not reported,
mean age was 71
years
Davison et al.19
162 patients, age
range not reported,
mean age was 62.4
years
Davidson,
Brundage, &
FeldmanStewart107
22 patients, age
range not reported,
mean age was 65
Methods: design, sample, sampling,
setting; data collection
Cross-sectional, women with breast
cancer diagnosed in the past 6 months,
convenience sampling from two tertiary
referral centers; survey questionnaires
using INQ
Cross-sectional, men with prostate cancer
diagnosed in the past 6 months,
convenience sampling from a community
urology clinic; survey questionnaires;
INQ
Cross-sectional, men diagnosed with
prostate cancer recently and just had their
initial urologic treatment consultation,
consecutive sampling from a tertiary
hospital; computerized version of INQ
Cross-sectional, newly diagnosed lung
cancer patients with a median time of 8.3
months from diagnosis, consecutive
sampling from a gynecological service of
a large teaching hospital and oncology
unit; survey questionnaires; investigatordeveloped questionnaire on information
priorities
Top three patient priorities for
information related to TDM
1. Stage of disease
2. Likelihood of cure
3. Risks and benefits of various
treatments
1. Likelihood of cure
2. Stage of disease
3. Risks and benefits of treatments
1. Likelihood of cure
2. Disease stage
3. Risks and benefits of various
treatments
1. Treatment
2. Survival
3. Side effects of therapy
34
35
Table 2 continued
Reference
Beaver & Booth108
Sample size, age
range, mean age
53 patients, 24-82
years, 55 years
Gopal et al.109
100 patients, 32-60
years, 45.09 years
Li et al.110
374 patients, 31-85
years, 55.4 years
Degner et al.66
1,012 patients, age
range not reported,
58.25 years
Methods: design, sample, sampling,
Top three patient priorities for
setting; data collection
information related to TDM
Cross-section, newly diagnosed
1. Likelihood of cure
gynecological cancer patients; consecutive
2. Stage of disease
sampling from outpatient clinics of a large
3. Side effects of therapy
teaching hospital; structured interviews
using INQ
Cross-sectional, women who were newly
1. Likelihood of cure
diagnosed with breast cancer, between 3
2. Sexual attractiveness
and 4 months from diagnosis,
3. Stage of disease
convenience sampling from two major
hospitals; survey questionnaire using INQ
Cross-sectional, women diagnosed with
1. Likelihood of cure
breast cancer with a mean time of 19.7
2. Stage of disease
months since diagnosis, convenience
3. Risks and benefits of various
sampling from a large regional hospital;
therapies
survey questionnaires using INQ-Chinese
version
Cross-sectional, women diagnosed with
1. Likelihood of cure
breast cancer with a median time of 2.5
2. Stage of disease
years since diagnosis, consecutive
3. Risks and benefits of various
sampling from 2 tertiary oncology referral
therapies
clinics; survey, nurse-administered
questionnaire; INQ
35
36
Table 2 continued
Reference
Sample size, age
range, mean age
105 patients, 35-80
years, 56.02 years
Methods: design, sample, sampling,
setting; data collection
Cross-sectional, women diagnosed with
breast cancer assessed at time of diagnosis
and follow-up (mean time of 21 months
since diagnosis), consecutive sampling
from a consultant’s list in a large teaching
hospital; structured interview; INQ
Mistry et al.112
187 patients, 24-91
years, 58.8 years
Almyroudi et al.56
329 patients, age
range not reported,
59.5 years
Cross-sectional, men and women
diagnosed with various types of cancer at
pre- and post-treatment stage,
convenience sampling from outpatient
oncology clinics; survey questionnaires
using investigator-developed 35-item
Likert-type information needs categories
Cross-sectional, breast cancer patients
with a mean of 43.37 months disease
duration, consecutive sampling from a
large university general hospital; semistructured interview using ISQ
Luker et al.111
Top three patient priorities for
information related to TDM
At diagnosis:
1. Likelihood of cure
2. Stage of disease
3. Risks and benefits of therapy
At follow-up:
1. Likelihood of cure
2. Family risk
3. Stage of disease
1. Information related to cancer
domain
2. Information related to prognosis
domain
3. Information related to
rehabilitation domain
1. Information on investigative tests
that must be done, and when
2. Diagnosis of cancer
3. Exact treatment plan the doctor
has decided
36
37
Table 2 continued
Reference
Sample size, age
range, mean age
731 patients, 20-92
years, 61 years
Methods: design, sample, sampling,
Top three patient priorities for
setting; data collection
information related to TDM
Longitudinal, patients who had been
1. Information on likelihood of how
Hawkins et al.113
diagnosed with breast, lung,
the treatment will work
genitourinary, hematologic,
2. Information on how to relieve side
gastrointestinal, or head and neck cancer
effects
and whose treatment plan included
3. Information on how to ease the
chemotherapy or radiation therapy,
family’s stress
median time from diagnosis to baseline
interview was 39 days, convenience
sampling from a large university-based
cancer center; investigator-developed 5point Likert-type information needs
questionnaire
15 patients, 58-86
Cross-sectional, patients newly diagnosed
1. Tests/treatment
Andreassen et
years, 69 years
with esophageal cancer in the past 2-3
2. Health care professional’s
al.114
weeks, consecutive sampling from an
competence
outpatient clinic; survey using
3. Self-care
investigator-developed 5-point Likert-type
information needs questionnaire
Abbreviations: ISQ – Information Style Questionnaire;102 INQ – Information Needs Questionnaire101
37
38
Demographic Factors Associated with Information Needs Priorities in Cancer Patients
Sociodemographic factors have been examined for their association with patients’
information needs priorities. One study reported that patients’ preference for more
information relating to prognosis was associated with being male.60 Two studies66, 87 reported
that information about sexuality and physical attractiveness was more important to younger
women (≤ 50 years) than older women (≥ 50 years) (p<.001). Luker and colleagues also
reported similar findings.104 Similarly, Davison and colleagues103 reported that young men (<
65 years) ranked information on sexuality as more important than older men (≥65 years).
Moreover, older women (≥60 years age group) rated information pertaining to social life as
more important than did younger women (p = 0.03).104 Bilodeau and Degner89 reported
higher age (specifically within the 65-85 year-old category) to be significantly associated
with a higher ranking for self-care information (p ≤0.02).
One study reported that women with less than a high school education wanted more
information on self-care (p = <0.02) than did women with higher education,89 while another
study reported that women with less than high school education wanted relatively more
information on likelihood of cure.87 However, two other studies found no difference in
information needs by educational level.66, 104
Davison, Degner and Morgan106 reported that single men ranked information on selfcare significantly higher than did married men (p<0.001). One study reported no association
between age, level of education or partner status and information needs priorities.19
39
Conclusion
Treatment decision making in cancer involves information giving, processing of
information, weighing of risks and benefits of therapy, and patient participation or nonparticipation during the decision making process. This review highlighted the relevant
physician and patient factors that could influence treatment decisions in cancer patients.
Physician-related factors include physician’s beliefs, attitudes, and values; medical expertise
and specialty; patient’s medical and clinical conditions; communication style and medical
literature. Influential patient-related decision factors include patient’s beliefs and values;
ethnicity; decisional role preferences; personal factors; context; and perception of the
decision-making process. When patient and physician decisional factors differ in the setting
of clinical uncertainty, it is critical that adequate time is allotted for effective communication
between physicians and patients to account for the influential personal and contextual factors
from both perspectives. Effective communication exchange is essential for the achievement
of individualized treatment decisions.
Frameworks or models of decision making have essentially focused on the patient’s
roles during the decision-making process. There is a growing trend among patients of
wanting to be informed and involved with the TDM process, as evidenced by the high
percentage of patients who prefer a shared and active role over a passive one. Several
decision-making studies have been conducted in breast and prostate cancer populations that
already bear this trend out. However, more such studies are now being conducted in
colorectal, lung and gynecological cancer patient populations. Unfortunately, there is still a
paucity of decision-making research in hematologic and other solid organ malignancies.
40
In the area of information giving, this review reveals that decision researchers
consistently find that there is a discernible priority of information needs among cancer
patients, needs that include prognosis, diagnosis, treatments, side effects, and self-care.
Although clear patterns emerge in this research, there are still huge variations in patients’
reported priorities of information needs. Moreover, it has been found that preferences and
priorities do change over time for individuals, depending on stage of the disease and other
factors. Interestingly, sociodemographic factors do not always factor into patient preferences
for TDM participation and information needs. Therefore, it is critical that health care
professionals assess their patients’ individual preferences for TDM participation and priority
of information needs across the entire care continuum. Finally, since treatment decisionmaking primarily involves the physician and the patient, it is essential for decision
researchers to examine both patient-related and physician-related factors simultaneously in
order to understand how the interaction of these two factor group ultimately impacts
treatment choice.
41
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55
CHAPTER III
Patient and Physician Perspectives on Treatment Decision Making in Older Adults
Newly Diagnosed with Myeloma
Introduction
Multiple myeloma (MM) is a cancer of the plasma cells. It affects primarily the
elderly, with the highest incidence occurring at the sixth through the eighth decade of life.1 In
2010, there were approximately 20,180 new cases diagnosed in the U.S., making it the
second most common hematologic malignancy after non-Hodgkin’s lymphoma.2 The overall
annual incidence rate of myeloma in the United States from 1973-2005, age-adjusted to the
2009 population, was 11.0 and 4.3 per 100,000 person-years for blacks and whites,
respectively.3 MM has worldwide incidence rates from 0.4 to 5 persons per every 100,000 on
a given year, with rates being higher in Western than in Asian countries.4, 5 MM is not
curable; however, there are many effective treatments available that can extend patient
survival with relatively good overall quality of life.6
Randomized controlled trials (RCTs) have compared conventional chemotherapies
with high-dose chemotherapies followed by an autologous hematopoietic stem cell
transplantation (HSCT). The results of these trials show equivocal results in terms of overall
survival, though they do show that the chemotherapy plus autologous HSCT combination
treatment offers longer progression-free survival.7, 8 Autologous HSCT is still widely
accepted as a treatment option for myeloma patients <65 years of age, yet it is now being
challenged through historical control studies and randomized controlled trials comparing
outcomes from intensive therapy versus non-intensive therapy using novel drugs.9-11 RCTs
comparing the use of novel treatment agents with or without conventional chemotherapies
56
against conventional chemotherapies are underway. However, to date there are no RCTs
comparing high-dose chemotherapy followed by HSCT with novel therapies.11
Older adults newly diagnosed with multiple myeloma have multiple treatment options
available, each with potentially different clinical outcomes and risk/benefit trade-offs.
Evidence-based treatment guidelines developed by the National Comprehensive Cancer
Network (NCCN), an organization of 21 leading comprehensive cancer centers in the U.S.,
do not identify one treatment as unequivocally superior to all alternatives for a given set of
conditions, based on available data.12 Myeloma treatments also come in various forms,
routes, and intensities, including oral, IV, and high-dose chemotherapies or reduced intensity
conditioning followed by HSCT. These chemotherapeutic regimens have different patient
adherence and completion rates. Other factors, such as direct cost to the patient (co-pays and
deductibles) and insurance coverage status, may also influence treatment considerations. It is
unclear how these variables ultimately influence actual treatment choices in older adults
newly diagnosed with myeloma.
Given the lack of one recognized “best” medical therapy, patients are in a position to
choose one or more treatments among the many available options. This notion of patient’s
treatment selection comes with the assumption that physicians offer options to their patients.
Whether or not patients actually participate in their treatment decision, they are still vested in
the outcome. In other cancer diagnoses where adults have multiple treatment choices, there is
strong evidence that personal factors and preferences are quite influential in determining how
patients arrive at a final treatment decision.13, 14 Similarly, physician preferences and values
have also been found to be influential in actual treatment choices.15-17
57
The clinical uncertainties in patients newly diagnosed with myeloma can be largely
attributed to the existence of multiple possible outcomes, each with unknown probabilities.
Adding to the complexity is the presence of new and emerging scientific data and difficulties
for physicians in applying clinical trial data to particular patients. Moreover, advances in
myeloma genomics are beginning to shed some understanding on the role of genetic
aberrations in the success rates of various myeloma therapies, adding still more complexity
and uncertainty to the treatment decision-making (TDM) process.18, 19 The advent of novel
drugs showing similar (or sometimes better) response rates when compared historically to the
outcomes from standard therapies creates further clinical uncertainties in treatment decision
making.20-23
Research studies that examine not only the physician’s perspectives, but also those of
the patient, can inform both clinicians and policy makers on how to improve outcomes
related to cancer treatment decisions. By examining and understanding patient preferences
and values, clinicians will be better prepared to engage in shared decision making with
patients diagnosed with myeloma. Information on treatment decision making is particularly
relevant for the elderly with myeloma, who may have a different set of values and
preferences than younger patients. Conversely, by understanding physician perspectives,
policy makers and medical practice administrators will have a broader view of the process
and may be able to support innovative strategies that will enhance physician-patient
treatment decision encounters.
Treatment Considerations in Older Adults
There are specific treatment considerations in older adults with cancer, particularly
those who are 60 years of age or older. The patient’s age is likely to be an influential factor in
58
treatment decision making by both patients and clinicians. Berry and colleagues24 found
evidence of this association in a study of 260 men with localized prostate cancer. A majority
(70%) of the study participants reported that their age had influenced their treatment
decision, with older men being more likely to eliminate a particular treatment option
exclusively because of their advanced age. In addition, several studies have found that
clinicians will either rule out particular treatments for patients with colorectal or breast
cancer based on a patient’s age or will give strong recommendations against particular
treatments.25-29
In a recent survey30 of physicians who were involved in treatment decisions regarding
chemotherapy in cancer patients ages 70 years and older, treatment side effects (24.4%),
multiple illnesses (20.5%), and lack of support from family and friends (10.9%) were
reported as challenges. The authors reported that in addition to the presence of comorbidities,
functional status was among the principal factors physicians considered when they made
such treatment decisions.30
Older patients are at a higher risk for chemotherapy toxicities due to physiological
changes associated with aging, potentially causing adverse quality of life (QOL) outcomes.31
QOL, comorbidities, treatment tolerance, and life expectancy have been proposed as
important elements in the treatment decision equation of older adults with cancer.32-34 In
fact, older adults have ranked QOL as a top priority in life.35 When asked about the
importance between QOL and quantity of life in relation to their treatment decisions, 97%
(N=42) of older adults (age range 60-85 years, median 71 years) with acute myeloid
leukemia or advanced myelodysplastic syndrome shared that QOL, rather than length of life,
was an important factor in their choice of therapy.36
59
The elderly population is growing rapidly and life expectancy is rising; therefore,
studies that examine the impact of QOL, comorbidities, and life expectancy on treatment
selection are increasingly becoming critical. How these various factors are considered in the
context of patient-physician interactions during treatment decision making has not been
explored in the past. In summary, there is strong evidence that older patients with cancer do
have personal preferences and values and contextual factors influencing their treatment
decisions. There is also considerable evidence that patients want to be informed and
consulted with regard to the impact of treatment on quality of life as well as survival.
However, no data exist regarding influential factors that older adults diagnosed with
myeloma consider when they make treatment decisions. Moreover, physician factors
influencing treatment decisions have not been previously studied in older adults newly
diagnosed with myeloma.
Purpose
The purpose of this study was to explore patient- and physician-related factors
influencing treatment decisions in older adults newly diagnosed with myeloma. The first
objective of the study was to examine the patients’ perspectives on treatment decision
making, including their personal and contextual factors relevant to treatment decision
making. The second objective was to describe physicians’ perspectives on the treatment
decision making in older adults (≥60 years of age) newly diagnosed with myeloma.
Methods
Design
The study employed a descriptive, cross-sectional design using semi-structured
interviews. Since treatment decision making is a complex health care phenomenon that has
60
not been elaborated in patients with multiple myeloma, a qualitative approach was used to
explore the perspectives of patients and physicians during treatment decision making and to
examine the factors influencing treatment decisions from both perspectives.
Sample
The patient sample consisted of 20 older adults ages 60 years and above who were
newly diagnosed with myeloma and had been referred to Seattle Cancer Care Alliance
(SCCA) or the Northwestern University Myeloma Program (NUMP) by
hematologists/oncologists in the greater Seattle or Chicago areas, respectively. To be eligible
for the study, patients had to be (a) older adults (60 years of age and above); (b) newly
diagnosed (6 months from diagnosis) with multiple myeloma; (c) able to read and write
English; (d) able and willing to give informed consent. The physician sample consisted of
five physicians from SCCA and University of Washington (UW)-affiliated clinics and five
physicians from NUMP who were directly providing care to myeloma patients.
Patient and Physician Recruitment
After obtaining approval from the UW and Northwestern University Human Subjects
Divisions (HSD), older adults newly diagnosed with myeloma were recruited to participate in
the study. The researcher made every attempt to recruit from both university- and
community-based practices to enhance the diversity of study participants. Older adults ages
60 and older, who had an appointment at either SCCA’s, UW-affiliated clinics’, or NUMP’s
hematology or transplant services and were otherwise eligible for the study based on clinic
records were recruited by mail using a recruitment flyer (SCCA or UW clinics) or a direct
approach (NUMP). Clinic schedules were reviewed weekly to identify potential study
participants. A total of 79 potential participants at SCCA and UW-affiliated clinics were sent
61
recruitment letters from October 2009 through July 2010. Of these 79 potential participants,
14 responded to the mailer (17.7 % response rate), and all agreed to participate in the study.
At NUMP all six potential participants approached by the researcher agreed to participate.
Physicians were recruited via e-mail and by direct approach. A total of 16 physicians were
approached from SCCA, UW-affiliated community clinics, and NUMP. Three physicians
from SCCA, two physicians from UW-affiliated clinics and five physicians from NUMP
agreed to participate (62.5% recruitment rate). Informed consent was obtained from all study
participants. No enrolled participants withdrew from the study before complete data were
collected.
Procedure
A semi-structured interview was conducted in a designated research-related
conference room at SCCA and NUMP. These rooms were strictly assigned for research use
only and met the HSD’s standard for patient privacy. If a patient wanted the interview to be
conducted at a later time, a one-week period following the clinic appointment was allowed
for re-scheduling. Moreover, if a patient requested, the researcher conducted the interview at
his or her home. Similarly, the physician interview was conducted in a place where privacy
was secured, generally the physician’s office.
Patient participants were asked about the treatment options discussed by their
physicians including risks and benefits, their preferred role during decision making, and how
they make the best treatment decisions for themselves. Physician participants were asked to
recall their experiences of how they usually presented treatment options to patients. They
were then asked specifically about which factors they consider when making a treatment
62
decision, their preferences and perceptions of patient participation during the decisionmaking process, and how they make the best treatment decision for their patients.
All study interviews were audio-recorded and then transcribed verbatim. Identifying
names or proper nouns were not included in the transcription. All transcripts were checked
against the original audio recording for accuracy.
Analysis
Directed content analysis procedures37 were used to develop major themes from the
patient and physician participant interviews. Initial categories and their definitions were
developed based on a literature review of physician and patient factors influencing treatment
decisions in cancer. Interview text was read line by line by the lead investigator (JDT) and
then imported to NVivo 8 (QSR International, Victoria, Australia),38 a qualitative data
analytic software program. Initial categories and definitions were also imported to NVivo.
The minimum unit of analysis was typically one sentence, but sometimes the unit was an
entire paragraph, depending on whether the participant shifted the topic in a different
direction other than what was asked in the interview schedule or in follow-up probes. Probes
included statements such as, “Tell me more about the role you have selected,” or “What else
are the influential factors in your treatment decisions?” If the interview text matched the
definition of a pre-established category, that code was assigned to the text. Text that could
not be coded within the initial categories was given a new category and definition. Some
categories were grouped together to create major themes. Interview transcripts were re-coded
based on the subsequent identification and definition of these new themes or other new
categories.
63
Full agreement between investigator JD and BC in terms of coding scheme and their
definitions was reached utilizing the process of consensual validation.39 Initial and emerging
categories were reviewed and discussed among three members of the research team (JD, BC,
and DLB). Ongoing in-depth discussions and agreement about the wording of final themes,
factors encompassed by major themes, and their definitions were carried out by the
investigators, JD and BC. The percentage agreement for the coding of patient interview
themes and factors was 86.7% and 81.4%, respectively. For physician interview themes and
factors, the percentage agreement was 91.7% and 86.1%, respectively. Given the exploratory
nature of this study, the degree of agreement for coding that has been achieved between
investigator JD and BC is considered acceptable.40 Nine major themes were identified from
the patient interviews and seven major themes were identified from the physician interviews.
Results
The patient participants sample mean age was 67.5 years (SD=7.6); mostly Caucasian
men and women participated, with only one Asian and one Native American. The sample of
ten physician participants consisted mainly of women (n=7) between 30 and 39 years of age
(n=8), but with varying race/ethnicity and title or position in the institution. Tables 1 and 2
list all demographic information collected for the patient and physician participants,
respectively.
64
Table 1. Sociodemographic Characteristics of the Patient Sample
Variable
Age group (mean 67.5 years)
60-70
71-82
Gender
Male
Female
Race
Caucasian
Asian
American Indian/Alaskan
Native
Employment Status
Full time
Working, on medical leave
Not working
Retired
Student
Personal Relationship Status
Single
Married or partnered
Divorced
Widowed
Highest Level of Education
9th – 12th grade
2 years of college
4 years of college
Graduate degree
Annual Household Income
$18,000 or less
$18,000 to $35,000
$35,001 to $55,000
$55,001 to $85,000
$85,001 and above
N
%
14
6
70
30
8
12
40
60
18
1
1
90
5
5
2
2
2
13
1
10
10
10
65
5
2
12
5
1
10
60
25
5
5
2
10
3
25
10
50
15
3
2
5
5
5
15
10
25
25
25
65
Table 2. Sociodemographic Characteristics of the Physician Sample
Variable
Age group (mean??)
30-39
40-59
Gender
Male
Female
Race
Caucasian
Asian
African American
Title or Position
Fellow
Attending Physician
Private Practice Physician
Personal Relationship Status
Single
Married or partnered
N
%
8
2
80
20
3
7
30
70
5
3
2
50
30
20
5
3
2
50
30
20
1
9
10
90
The major themes with their definitions and frequencies of occurrences for both
patient and physician interviews are shown in Tables 3 and 4.
Table 3. Major Themes from Patient Participant Interviews on TDM
Themes
Trust in the physician,
health care team,
and/or institution
Definitions
Participants verbally expressed their trust in the
physician, the health care team, and/or the
institution as influential in treatment decisions.
This definition also includes implicit trust to the
physician by going along with physician’s
treatment recommendations or decisions.
N
(%)
20
(100)
66
Table 3 continued
Themes
Participants have
many sources of
information related to
myeloma
Participants have
various decisional role
preferences
Patient-specific factors
influence treatment
decisions
Negative perceptions
of treatment decision
making
Definitions
Participants described the different sources of
myeloma-related information such the Internet,
physicians, family and friends who help do the
research and obtain myeloma-related materials,
physician visit companions, books, pamphlets,
nurses, myeloma patients, other cancer patients,
and support group such as the multiple myeloma
fighters and myeloma or lymphoma society. This
is distinct from “other’s opinions” in which the
information or opinion was identified as actually
influencing the treatment decision
Patients described their role preferences or any
changes in role preferences as being active
(patient making his or own treatment decision
with or without consideration of the physician’s
opinion), shared (patient and physician share
responsibilities in making the treatment decision),
or passive (patient delegating the treatment
decision to the physician) or changes in their role
preferences outside of the context of being in a
state of shock.
Patient-specific factors refer to patient’s actual
experience with myeloma-related therapy, age,
beliefs and values, faith in a higher power,
opinions of family, opinions of others, past
health-related experience not related to myeloma,
and self description of “What I’m like”
influencing treatment decisions. This theme will
be coded along with the patient-specific factor.
Patients described negative perceptions of
treatment decision-making such as lack of
discussion of treatment options, long periods of
waiting during the encounter, inability to reach a
health care team member, and wanting to have
more information related to disease, prognosis,
treatment, and side effects or having questions
left unanswered, not achieving the desired level
of participation
N
(%)
20
(100)
20
(100)
19
(95)
17
(85)
67
Table 3 continued
Themes
Treatment decisions
are driven by the
benefits of being
cancer-free, the desire
to be in remission, and
the desire to live a
longer life
Contextual factors
influence treatment
decisions
Some participants
were in a state of
shock at the time of
diagnosis
Advances in science
provide hope for
future treatment
options
Definitions
Patients described the benefits of their therapy
such as being cancer-free, killing the cancer cells
(patients also describe their myeloma marker at 0
level), being in remission, and living a long life
Patients’ contextual factors refer to issues of
health insurance, financial status, availability of
free medication regardless of insurance,
geographical barriers, treatment costs, social
support, housing/lodging, retirement planning,
recent significant events in the family, and
transportation/convenience of oral therapy at
home influencing treatment decisions. This theme
will be coded along with the specific contextual
factor.
Participants described being in a state of shock,
feeling very overwhelmed and not at the right
frame of mind, unable to process what was heard
from the physicians during the visit, feeling pretty
much out of it or kind of in a fog, and feeling
paralyzed from participating in treatment decision
making
Participants described advances in science
provide hope for future treatment options but not
influencing their current treatment decision
N
(%)
15
(75)
14
(70)
6
(30)
4
(20)
68
Table 4. Major Themes from Physician Participant Interviews on TDM
Themes
Definitions
Some physicians describe QOL or survival
consideration and some physicians
simultaneously consider multiple factors
including efficacy, QOL, and survival in their
treatment decisions. This theme is also coded
when the physician mentions morbidity,
mortality, and life expectancy considerations
when making treatment decisions.
These are aspects of the physician’s life that
Physician-specific
influence treatment decisions. These factors
factors influence
include patient’s context, patient’s family
treatment decisions
opinion, patient’s co-morbidities, functional
status, and supportive care requirement, patient’s
treatment preference, patient’s age, patient’s
medical and clinical factors, physician’s beliefs
and values, and physician’s expertise and type of
practice. This theme is coded in conjunction with
the specific physician factors.
Physicians evaluate their patients’ overall medical
Eligibility for
autologous HSCT is an condition for eligibility for autologous stem cell
transplantation
important treatment
consideration
Physicians use various Physicians share that they have no systematic tool
to assess preference; sometimes they ask or
ways of assessing
patient decisional role sometimes they indicate they just have a feeling
for the patient’s role preference. This theme does
preferences
not include the physician’s own description of the
decisional role of patients.
Physicians describe limited time and lack of long
Barriers to effective
term outcome data as barriers to effective
decision making
decision making; physicians share that there is a
need to spend more time talking with patient
The patient ultimately Physicians describe providing different treatment
options to patients, explaining risk and benefits,
makes the treatment
and specifically state that patient ultimately
decision
makes the final decision. This does not include
physician’s belief on patient participation or nonparticipation with decision making
Physicians consider
QOL or survival alone
or simultaneously
consider QOL,
treatment
effectiveness, and
survival
N
(%)
10
(100)
10
(100)
9
(90)
9
(90)
7
(70)
5
(50)
69
Table 4 continued
Themes
Definitions
N
(%)
2
(20)
Physicians describe presenting strong treatment
When needed,
recommendations to patients when patients make
physicians attempt to
illogical decisions
persuade patients to
take the physician’s
recommended
treatment option
Abbreviations: QOL = quality of life; TDM = treatment decision making; HSCT =
hematopoietic stem cell transplantation
Major Themes from Patient Interviews
The major themes from the patient participant interviews were organized according to
the highest to lowest frequency of coding and are presented below.
Trust in the Physician, the Health Care Team and the Institution
All participants considered the physician’s opinion as having an influence on their
treatment decisions. Twelve out of twenty participants explicitly said they trusted their
physicians immediately at the time of diagnosis and accordingly went along with the
physician’s treatment decision. Eight participants indicated implicit trust of their physician’s
treatment decision by going along with the physician or not questioning the physician’s
treatment decisions. Additionally, in cases where a physician had expressed a preference for
a particular treatment, no patient participants described going against the physician’s
preferred therapy. Finally, based on the physician’s evaluation of their myeloma status,
patient participants tended to agree with the next treatment direction the physician suggested.
Regardless of the physician practice type or specialty, there was a consistent theme of trust in
the physician. One patient participant explicitly mentioned trust in the physician’s team and
the institution (NUMP).
70
Patient 005: “I really want to trust his expertise because he knows a lot more about it
than I do, so he probably has more input than I have.”
Decisional Role Preferences
Participants expressed various decisional role preferences. Half of the participants
preferred to make the decision themselves, while 45% said they would have liked to share the
responsibility of making the decision with their physician. Only one patient participant
brought up the idea that she would have liked the physician to make the decision while
seriously considering her opinions about treatment before making a recommendation.
Sources of Information Related to Myeloma
When patient participants were asked where they get their information related to
myeloma and its therapy, various sources, including internet (n=12); physicians, both
primary oncologist and second opinion physicians (n=7); family and friends (n=4); books
(n=3); pamphlets (n=3); nurses (n=2); myeloma patients or other cancer patients (n=3). Some
patients reported two or three different sources of information at any given time.
Contextual and Patient-specific Factors Influence Treatment Decisions
When asked how they make the “best treatment decision,” participants described
larger contexts in their lives, talking about factors such as finances (impact on work,
retirement), logistics (transportation, housing/lodging), availability of family/caregiver
support, existence of an elderly spouse or another sick person of the family, living status
(living alone), and geographical advantage (living near a transplant center) as being
influential in their own treatment selection. An exemplar quote from a patient who decided to
have intravenous bortezomib therapy instead of oral chemotherapy:
Patient 013: “I have someone to drive me to the clinic; my wife does that. I drive over
and she drives back. I feel capable of driving back, but my concern is that if I’m in an
71
accident and they learn that I just came from a clinic, something will be wrong with my
responses, so we don’t take that chance. I just let her drive me back and that works fine.”
Aside from contextual factors, there are several patient-specific factors that they
mentioned as influential in their treatment choice. These factors pertain to the individual’s
recall of influences on their actual treatment selection. Tables 5 and 6 outline these patientspecific and contextual factors, respectively.
The most common patient-specific factors influencing treatment decisions were
actual experience and the value placed on quality of life. Patient participants described how
their actual experience with therapy influenced their treatment decisions. They were able to
recognize that, when they are not responding to therapy, a change of therapy is most likely
going to happen. Additionally, when they are experiencing unacceptable toxicity from the
therapy, it also meant modification in their treatment plan or change of therapy. The patient
participants wanted to live a physically active and productive life, continuing to do things
such as driving, skiing, traveling, and spending time with family and friends. They
considered the most appealing therapies to be those that would not make them feel sick all
the time with nausea, vomiting or pain. They preferred not to have blood transfusions several
times a week. Participants also said that, if it was possible, they did not want to visit the
clinic/physician several times a month. Oral chemotherapy was preferred by six patient
participants who were above the age of 70 years. They did not want to continue therapy if it
meant not being able to do the things they loved. A patient participant said, “If I needed to
spend 8 years in a wheelchair, with no one to look after me, by myself somewhere, not being
able to paint, not being able to write, I don’t see the point of it.”
72
Table 5. Patient-specific Factors that Influenced Treatment Decisions
Factors
Actual
experience
with
therapy
Beliefs and
values
Opinions of
family
Definitions
Participant’s actual experience
with therapy specific for their
myeloma such as reaction, side
effects, response or nonresponse to therapy influence
subsequent decisions. This
definition does not include
experiences with therapies not
related to myeloma (included
in the definition of past healthrelated experiences)
Participants’ personal belief
about the necessity of
completing a therapy or belief
in the outcomes of a specific
therapy and the participants’
valuation of QOL,
independence, and not being a
burden to family as influences
on their treatment decisions.
Beliefs and values are different
from the perceived benefits of
treatment
Participants’ solicitation and
consideration of the opinions
of family influenced treatment
decisions; a specific link
between family opinions and
treatment choice was identified
Exemplar Quotes
“I had unfavorable reactions to
the medication I was taking. I
started developing neuropathy
in my hands and feet. My
doctor consulted a myeloma
specialist and my treatment
was changed.”
N (%)
16
(80)
“I just think this cancer is very
tricky and that we have to outtrick it. So I think novel
treatments are where I see the
greatest potential of a longer
life for people like me. That’s
why I chose the clinical trial
involving novel agents.”
16
(80)
“My children very much
wanted me to fight this to the
bitter end and regain my life
back, because I think it was
very hard for them to see me in
an invalid kind of stage where
they had to care for me at the
beginning, and I’m always the
one caring for them.”
9
(45)
73
Table 5 continued
Factors
Age
Definitions
Participants who described
themselves as being at a
particular age category and
feeling healthy or being in a
particular age, regardless of
health status, had influenced
their treatment decision
Participant’s solicitation and
consideration of the opinions
of non-family members
influenced treatment decision;
a specific link between
opinions of others and
treatment choice was identified
Exemplar Quotes
“Well, the option that was not
seriously considered was stem
cell transplantation; because of
my age [80 years] that was
ruled out.”
“The thing that did influence
me a lot was I talked to a
gentleman during chemo that
had gone through the stem cell
and he was telling me how he
had eight absolutely wonderful
years where he traveled and he
was free of cancer and he got
his life back.”
"I had 21 operations in my life.
Past health- Participant’s own past healthrelated (e.g., overall good
I’ve come through all that.
related
health,
past
illness
experience)
This is just another step in my
experience
and therapy-related
life and I'll come out the other
experiences (not relating to
end smiling. So that is why I
myeloma) influenced
decided to take the decision of
participants’ treatment choice
having an auto stem cell
transplant.”
“I didn't see any reason to
“What I’m Participants who identified
what they were like as a
question there was an
like”
person—their job, their
alternative to this cutting edge
personality--influenced
treatment [auto stem cell
treatment decisions
transplant]. It seemed to me
I'm a cutting edge guy so it
appealed to me.”
Participants described praying “I prayed to God to give me
Faith in a
to a higher power and faith in a the best treatment and the best
higher
higher power as an influence in doctor.”
power
their treatment decision
Opinions of
others
N (%)
9
(45)
8
(40)
6
(30)
5
(25)
5
(25)
74
Table 6. Contextual Factors that Influence Treatment Decisions
Factors
Definitions
Social
support
Availability of family and/or
friends to provide caregiver
support during therapy-including attendance at the
physician or clinic visits or
availability of family members
to take some household
responsibilities, being single,
family, and caregiver burden-influenced treatment decision.
The type of insurance coverage
for a particular therapy
influenced participants’
treatment choice.
Insurance
Transportation
issues/
Convenience of
pills
Geographic
barrier
Exemplar Quotes
“My wife is an absolute jewel.
She insists on taking care of
my every need and she invents
some needs I probably don’t
think about. My concern is if
she will wear down. She’s 83
and she has a lot of energy and
she’s a wonderful caregiver,
but I worry that I may wear her
down.”
“We were glad that we have
very good insurance coverage.
For two transplants, I was
adamant that any procedure I
do, it has to be certified and
pre-approved [by the insurance
company].”
Travel issues from home to
“Basically my doctor offered
clinic to home--such as
either the IV or the Revlimid
availability of a driver or the
by pill. I felt that well, I guess
ability of the participant to
the pill was more appropriate
drive a car, or the convenience for quality of life from my
of taking oral chemotherapy at standpoint. It's easier to do, it
home--influenced treatment
gives me more flexibility, and
decisions.
I don't have to keep going in to
the clinic every day.”
Participants described the
“I considered Mayo. I have a
actual distance and amount of
relationship with them from
time to travel to get the therapy many years ago, but I wanted
influenced treatment decision
to be in town [Chicago]. I'm
and the need for housing or
impressed with the team in
lodging during therapy due to
Northwestern facility and it
distance of medical institution also has the advantages of it
for the participant’s residence. being near my three daughters
and my wife so that I didn't see
any reason to go anywhere
else.”
N
(%)
9
(45)
5
(25)
5
(25)
5
(25)
75
Table 6 continued
Factors
Definitions
Finances
The participant verbally
expressed that the amount of
money the participant has to
pay out of his own pocket to
get the therapy and costs of
retirement influenced
treatment decision. This factor
includes participants getting
free medication instead of
paying several thousands of
dollars for their chemotherapy
Participants described how
significant events or conditions
in the family such as another
sick member of the family or
recent family death influenced
treatment decision.
Significant
events in
the family
Exemplar Quotes
“I'm running on empty
[finances] now. So I do take
that into account.”
“My mother is ailing and she's
very old, and we don't know
what our family needs are in
taking care of her needs. But I
have wanted to avoid having a
transplant, if possible.”
N
(%)
5
(25)
3
(15)
Negative Perceptions of the Treatment Decision-making (TDM) Process
The majority of the patient participants shared negative aspects of TDM. The most
common negative observations they made were their physicians being too busy and lacking
the time to adequately discuss treatment options or side effects of therapy. Fourteen percent
of the participants thought the physician did not present treatment options and that the side
effects of therapy were not discussed adequately. One participant thought her concerns were
not addressed at all.
Patient 008: “The first time we saw [the doctor], we waited and waited and waited
and waited and thought, "Oh my gosh, maybe we should just leave." So she's just a very busy
person.”
More than half of the participants expressed that some aspects of their information
needs were not met by their physicians. These needs related to the current and subsequent
treatments; overall survival; future therapies; diagnostic and monitoring tests; side effects
76
and their management and just a general overview of myeloma, including its causes and
genetic impact on the family.
Patient 001: “I think [information about] the treatment, length of treatment was very
iffy—it was kind of like: Let’s see what happens. And possible side effects were not really
discussed.”
Treatment decisions are driven by the benefits of being cancer-free, the desire to be in
remission, and the desire to live a longer life
When patient participants were asked to share their understanding of the benefits of
the treatment they currently have, the majority (75%) stated that they chose the treatment
they had because it was presented to them by the physician as their best chance to be cancerfree, to be in remission, and to get them back again to living a longer life. The patient
participants were cognizant that currently there is no cure for myeloma and that their
treatments offer them a period of remission, but relapses are inevitable.
Patient 001: “My doctor was very positive about the effects of the stem cell transplant
being the only way to achieve any kind … [pause] … Well, I was told this is a non-curable
disease, which I know. He told me the benefits of a longer remission, putting me to a more
normal life expectancy.”
State of Shock at the Time of Diagnosis
Six of the participants described themselves as being in shock when given the
diagnosis of myeloma. They described their reactions as “pretty much being out of it” and
“kind of in a fog” when told about the myeloma diagnosis. One participant said, “This is just
almost too much to take and I didn’t want to live anymore.” For these participants, it was just
too much to cope with the diagnosis and they were unable to participate in the decisionmaking process.
77
Patient 007: “I was kind of in a fog when it hit me. I was just kind of … I brought my
wife so she could hear what was being said and I’m glad I did, because a lot of things were
being explained that I didn’t pick up on because of the—I guess for lack of a better word—
the shock of being diagnosed with cancer.”
Some patient participants did not require immediate treatment decisions at the time of
diagnosis. They were able to think about their options for a week or two and had
opportunities to discuss the different treatment options with their families and friends; some
had the time to seek a second opinion with another physician.
Hope for Advances in Science
Participants shared their perspectives on how they rely on science to help them have
more treatment options. They have a good understanding that the science of myeloma
therapy is constantly evolving and that there have been several new drugs approved as
treatments for myeloma. However, these patient participants did not make their decisions
based on the hope for advances in science. They made their decisions based on what
treatment options were currently available to them, with the hope that advances in science
will be available to them in the near future.
Patient 010: “The only wildcard in this whole medical history is that because
myeloma is a cellular disease involving a lot of genetic components, and that part of our
science is evolving very quickly, I assume that if I can keep myself alive for a few years that
the medical technology may change.”
Major Themes for Physician Interviews
Physicians Consider QOL or Survival Alone or Simultaneously Consider Treatment
Effectiveness, QOL and Survival
According to the physician participants, there were three main components of a
treatment decision: balancing of treatment success (i.e., achieving remission), QOL, and
overall survival or considering each component separately. One physician participant
78
recognized that some patients would want as much quantity of life as possible in the
beginning, but as patients realized that QOL might decline with aggressive treatment, they
would backpedal and re-balance their treatment goals, with QOL being given relatively more
weight. The majority of physician participants agreed that for the elderly patients, QOL is
more important than quantity of life; only one physician participant discussed remission and
survival without explicitly talking about QOL.
Physician 009: “I think most patients can agree that a minimal decrease in the quality
of life over a short term in exchange for potentially a positive outcome in the long term is
worth it. But again, you have to take that into effect of how long are we affecting the
patient’s quality of life? You don’t want to keep somebody alive, you know—we can keep
somebody alive nowadays as long as we want sometimes, you know, and you don’t want to
do that in the setting of a very poor quality of life.”
In order to strike a balance among treatment success, QOL, and survival, physician
participants described the many treatment decision factors that they consider when
recommending treatment options to patients. Additionally, when asked how they make the
“best decision” for their patients, the physician participants identified many factors that they
consider during decision making. Table 7 outlines the factors that physician participants
reported as influential in the treatment decision.
Table 7. Physician-specific Factors Influencing Treatment Decisions
Factors
Patient’s
co-morbidities, functional
status, and
supportive
care
requirements
Definitions
Exemplar Quotes
Physicians describe presence
of comorbidities, poor or good
functional status, and
supportive care issues
influencing choice of
chemotherapy approach
“Other medical conditions,
performance status, if they've
had complications with the
chemo’ before. Primarily
comorbid conditions and their
performance status would
make me choose a less
intensive approach.”
N
(%)
10
(100)
79
Table 7 continued
Factors
Physician’s
beliefs and
values
Patient’s
context
Definitions
Exemplar Quotes
Physician’s personal beliefs on
patient-physician relationship
dynamics; beliefs that the
patient does not have much
knowledge, need for
oversimplification, need for
slowly introducing myeloma
concepts, and patient asking
the physician to talk about
what treatment physicians
would choose if it were them;
beliefs that patients should or
should not participate in
decision making; belief that
myeloma decisions are
becoming more technically
difficult to understand for the
patient influence treatment
decisions
Physicians consider the
patients’ contextual factors
such as health insurance,
financial status, availability of
free medication regardless of
insurance, geographical
barriers, treatment costs, social
support, housing/lodging issue,
transportation/convenience of
oral therapy at home, and
treatment compliance issues as
influential factors in treatment
“I think that would still involve
me being very active—because
I believe in transplantation I
think that I would be—try to
be very convincing to—that
would be wise. Because that’s
the approach I believe in.”
“The patient’s overall situation
could alter my decision; their
ability to come to the doctor,
their ability to follow-up, could
decide whether I would pick a
non-transplant or transplant
approach.”
N
(%)
10
(100)
9
(90)
80
Table 7 continued
Factors
Definitions
Exemplar Quotes
Patient’s
medical
and clinical
factors
Physicians consider patientspecific medical and clinical
factors such as type of
myeloma, high-risk disease
features by cytogenetics,
fluorescent in situ
hybridization (FISH) test, or
genomics, positive response or
resistance to therapy, patient’s
actual experience with therapy,
and any end-organ damage as
influential factors in treatment
selection
Physician’s
expertise
and type of
practice
The physician’s expertise in
stem cell transplantation and
practice type (transplant
center) influence treatment
choice
Patient’s
age
Consideration of patient’s age
(at 70 or older) regardless of
other factors, influenced
choice of a non-transplant
option or very young myeloma
patients ages 40-50 years old
tending to have very
aggressive treatment such as
high dose chemotherapy. Agerelated issues in the context of
stem cell transplant discussion
was coded under eligibility for
HSCT
“The prognostic indicators of
the cytogenetics of the bone
marrow will also lead a little
bit your decision making, and
the treatment of multiple
myeloma in the sense that if
you have deletion 17, we know
that myeloma won’t be
responsive to thalidomide or
lenalidomide or the “-imides”
in general. Therefore, you
would start an induction
treatment with bortezomib
typically.”
“I think to some degree it’s our
own bias because we are a
transplant center and I and
most of my colleagues
continue to believe that for the
appropriate age and health
condition, transplant is of
value. And so I definitely
approach a new patient with
the idea of determining if
they’re a suitable patient for
transplant.”
“I choose certain drugs for
patients who are age 70 and
over; certainly over this age we
would consider non-transplant
drugs.”
N
(%)
7
(70)
6
(60)
6
(60)
81
Table 7 continued
Factors
Patient’s
treatment
preference
Family
opinion
Clinical
trials as an
option
Definitions
Exemplar Quotes
Patient’s expressed preference
for a specific therapy
influenced treatment choice
“If they [patients] say they just
want pills and they understand
all of the upsides and
downsides, then sure.”
Family member’s opinion
“The older the patient the more
being weighed in by
likely you are to have other
physicians as an influence in
family members involved.
their treatment choice, but does More than just the spouse. It
not include physician’s
might be the daughter, or the
mention of the presence of a
kids involved. It is important
family member during patient- to get to the point where
physician encounters
everyone is comfortable and
understands what, and is in
agreement with the goals of
treatment.”
Offering a clinical trial as a
“For the patients over age 70
treatment option regardless of
or certainly over age 75, we’d
availability of free medication either be considering our
influenced treatment decisions clinical trial, which is
lenalidomide/dexamethasone
until progression, lenalidomide/dexamethasone for 18
months, or melphalan/prednisone/thalidomide.”
N
(%)
5
(50)
3
(30)
2
(20)
Eligibility for Autologous HSCT
When physician participants were asked about the treatment options available to older
adults newly diagnosed with myeloma, the majority (90%) said they check for patient’s
eligibility for autologous hematopoietic stem cell transplantation (HSCT). There were at least
four physician participants who considered the cut-off age for HSCT as being 70. Two
physician participants said they would still consider HSCT after the age of 70, but only if
their patients did not have comorbidities and had an excellent performance status. Only one
physician participant did not talk about HSCT and talked instead about non-transplant
82
treatment options, which is possibly due to the advanced age (over 70 years of age) of
myeloma patients under his care.
Physician 004: “Usually you break down the treatment options into considering if the
patient is a transplant candidate or not--if they’re a candidate for autologous stem cell
transplant. So usually, the patients who are 60 to 70 we might consider in that category.”
Barriers to Effective TDM
Some physician participants felt the time allotted for the clinic encounter is not
enough for adequate discussion of treatment options. One physician participant also thought
that explaining alternative treatment options to patients and allowing patients to express their
treatment preference are aspects of TDM that need some improvement. The physician
participants recognized that time management is an important aspect of patient care.
Physician 001: “Time is a major barrier, yeah. If you have to say… Listen, you have
to do everything in a half an hour—not possible—or even sometimes… You know, new
cases that come are usually an hour, an hour and a half with the attending … just one hour if
you’re just an attending sometimes is really not enough.”
Physician 007: “It's not really realistic. Especially if you have a lot of patients that
you're seeing in the clinic in a day. Then you might not have one hour to sit down with every
single patient and go over every single detail of the patient's treatment. So in an ideal world,
you spend one hour with every single patient, trying to explain every single detail. But
sometimes I think, because we have a lot of patients to look after, we basically are more—
our discussions with patients might be brief.”
Physicians Use Various Ways of Assessing Patient Decisional Role Preferences
When physician participants were asked whether they had a systematic way of
assessing a patient’s decisional role preference (e.g., use a tool to elicit patient’s decisional
role preference), nine out of ten responded that they do not have a systematic approach. The
physician participants commonly used various communication methods and observational
skills to gauge the decisional role the patient prefers during treatment decision making. Most
83
of the physician participants said they can tell what decisional role the patients might prefer
based on the kinds of questions they ask and sometimes by asking them directly.
Physician 003: “I don’t have a systematic way to do it, but usually by interviewing
them and meeting with them and talking to them, I think you can get a better sense of the
degree to which they want to be active participants in how they’re treated.”
The Patient Ultimately Makes the Final Treatment Decision
The majority of the physician participants (80%) described their role during treatment
decision making as being that of someone who presents the options to the patients and gives
them the opportunity to make the treatment decision. Most physician participants said they
tried to educate their patients, listen to them, and provide them with options that meet patient
needs or expectations. Two physician participants explicitly expressed that they will respect
the patient’s preferences and choices as long as effectiveness of therapy and overall outcome
were not compromised.
Physician 001: “In the end, it’s the patient that decides what he or she wants to do.
And so, what I can do is I can say to him, “There is this treatment, that treatment, these are
the risks involved with this treatment or that treatment, and these are the benefits. But
ultimately, it’s the patient who decides what he or she wants to do.”
When Needed, Physicians Attempt to Persuade Patients to Take Their Recommended
Treatment Option
Two physician participants stated that when patients do not want to do anything or
want to do something outside of their recommendations, they try to persuade patients to take
their recommendations. Additionally, when presenting options to patients, the physician
participants try to explain to their patients why they would favor one treatment option over
the others. The physician participants were cognizant that they cannot force patients to do
what they do not want to do, and physician participants emphasize that treatment decision
making is a joint process between the patient and the physician.
84
Physician 001: “Let’s say that if somebody comes with stage III high-risk multiple
myeloma and says, “We don’t want to do anything,” then we will strongly recommend—we
will strongly recommend doing something.”
Discussion
These findings document many themes and factors considered by physicians and
older patients with multiple myeloma during treatment decision making. There are some
similarities between the physician and patient results regarding influential factors for
treatment selection such as quality of life, convenience, insurance, cost, family opinion, age,
patient’s medical and clinical factors and social support considerations. These
multidimensional factors are simultaneously weighed by patients and physicians to make the
“best decision” in the setting of clinical uncertainty. Berry and colleagues41 have previously
reported similar personal factors that were influential in the treatment choices of men
diagnosed with localized prostate cancer, including age, cancer in the family, family
responsibilities and desire for longevity, as well as physician factors that included
consideration of the patient’s comorbidity and pathology.
Maintaining quality of life during therapy is very important, not only from the patient
participant’s perspective but also from the physician participant’s point of view. Among the
contextual factors described by patient participants, the convenience of oral chemotherapy
has been described as influential in the treatment decisions. Since a pill can be conveniently
taken at home, a decision for oral chemotherapy translates to fewer visits to the clinic,
ultimately impacting patients’ overall quality of life. This option is very attractive to older
adults above the age of 70 years participating in this study, because it offers them more
independence and requires less family burden to complete the therapy when they do not need
to ask family members to drive them to the clinic.
85
QOL and independence are values that are consistently ranked by older adults as their
top priorities in life.35, 36 Husain et al.42 have also reported quality of life and independence as
influential factors in the treatment choices by older women (>70 years of age) with breast
cancer. For physician participants in this study, oral chemotherapy was appealing as long as
the patients could adhere to the prescribed therapy and treatment efficacy was not
compromised. Kreling and colleagues43 have reported some similarities in what they called
patient’s contextual factors influencing treatment decision in older women (≥65 years of age)
with breast cancer, including the patient’s age, functional status, comorbidities and
perceptions of the benefits and side effects of chemotherapy.
The patient participants were making treatment choices based on contexts in their
lives. For example, the availability of support from family and friends influenced the
patients’ choice of intensive therapy. Intensive therapy requires a significant time
commitment and caregiver support during the acute phase of therapy (typically, days 1-30
post-transplant). The physician participants were cognizant of patient preferences and
contextual factors and offered treatment recommendations with strong consideration of the
patients’ overall personal factors and social contexts. The physician participants talked about
the importance of assessing a patient’s financial, logistical, and social support status and how
these factors influenced their treatment recommendations to their patients. One study44 has
reported family burden, cost, and travel requirements as important factors influencing the
physician’s decision to use adjuvant chemotherapy among older adults (≥65 years of age)
diagnosed with stage III colon cancer, but these contextual factors were not ranked as
important as patients’ comorbidities and medical evidence for treatment. There are some
similarities in the patient-specific factors (i.e., age, past health-related experience, insurance,
86
social support, family burden, geographic barrier) influencing treatment decisions when
compared to patient-specific factors of other older patients diagnosed with cancer. However,
some of the personal and social contexts (i.e., actual experience, some aspects of personal
beliefs and values, opinion of others, significant events in the family, convenience of oral
pills, and faith in high power) in this study varied from the personal and social contexts in
patients with breast,42 prostate,13 ovarian cancer,43 and colorectal cancer.44
At a certain point, age becomes influential in making a treatment choice; in general,
this study revealed that physician participants consider 70 as the cut-off age for more
intensive chemotherapies. In this study, patient participants also considered their particular
age as influential in their treatment choice. For example, patient participants who were 70
years and above (n=6) preferred oral chemotherapy and did not consider intensive
chemotherapy. The older patient’s preference for intensive or non-intensive therapy could
have been influenced by their physician’s recommendation, as this study has documented.
Kutner and colleagues44 have reported similar patient-reported considerations of the
physician’s opinion as influential in treatment decisions. Notwithstanding their findings,
some physician participants in this study would still consider intensive chemotherapy for
patients above the age of 70, provided that the patient had good performance status and no
comorbidities. Physician participants strongly considered treatment effectiveness and overall
survival, albeit while keeping an eye on the patient’s quality of life. The patient’s age has
been reported by both patient and physician as an influential factor in their treatment
decisions in patients with breast42 and prostate cancers.41
It was not surprising that both the patient (≤65 years of age) and physician
participants were considering intensive therapy. Intensive chemotherapy using high-dose
87
melphalan followed by autologous HSCT is considered an important treatment option for
myeloma patients under the age of 65.45, 46 For patients above the age of 65, it is unclear
whether physicians use a standardized decision analysis tool in their treatment
recommendations for intensive versus non-intensive therapy. Some physicians would not
recommend intensive therapy after the age of 70 years; others would still consider intensive
therapy beyond the age of 70, depending on the patient’s functional status and overall health
condition. The decision dilemma to recommend or not recommend intensive therapy to older
adults with myeloma has been raised in a review paper, where the authors of the paper asked
whether physicians are screening patients appropriately for intensive therapy.47 More studies
are needed to develop innovative methods to help both patients and physicians reach a
consensus on which treatment approach (e.g., intensive versus non-intensive) is best to take
in a given context. Furthermore, more studies are needed to help physicians develop clinical
decision pathways or treatment decision algorithms with regard to screening patients for
eligibility for autologous stem cell transplantation.
There was evidence of discordance between the physician’s and patient’s perspectives
with regard to who the decision maker actually should have been. The majority of the patient
participants perceived that the physicians made the decision for them. This belief ran
somewhat contrary to many physicians’ statements that it was ultimately the patients who
made the treatment decision. This is an interesting finding, and one that requires further
investigation. In patients who were in shock at the time of diagnosis, it is conceivable that the
physicians presented several options to patients, who were unable to recall the options that
were offered due to difficulty processing so much information. It is also possible that when
physicians made a strong recommendation for a particular option, the patients may have
88
perceived that they did not have enough knowledge about the different options and therefore
they would leave the decision to their physician, who ultimately made the decision for them.
In other qualitative studies in men diagnosed with prostate cancer48 and women with ovarian
cancer,43 the treatment decision also seemed to be driven mostly by the physicians, with
some patients perceiving themselves as passive recipients of care. Future research should
seek to uncover whether physicians can present treatment options with more equipoise and
whether patients who desire a more shared or active decisional role can be given the
opportunity by their physicians to participate more fully in the actual decision making.
Limited time has been reported as one of the barriers to effective treatment decisionmaking.49 The study findings described above have confirmed that this negative aspect of
treatment decision making was pervasive. Both patient and physician participants
acknowledged that the time allotted for treatment decision-making discussions was limited
and not sufficient to fostering the kind of discussion of options needed to reach a tailored or
“best” treatment decision. The onus is with the physician to provide patients ample time to
process the treatment options that they are offering to their patients. Poor quality patientphysician communication has been identified as one of the challenges in treatment decision
making50 and is also an area that requires further investigation. Studies that can guide
administrative policies on adequate time allocations, in terms of treatment decision
encounters, would be very beneficial not only for the patient but also for the physician.
Past health experiences and actual experience with myeloma therapy were found to be
influential in the treatment considerations by patient participants. These findings have been
previously reported in studies conducted in older women (≥70 years of age) with breast
cancer. For example, Husain and colleagues42 reported that two women in their study
89
strongly preferred a specific treatment based on their previous health care experience. The
researchers found that these women did not consider the treatment information provided by
their clinicians; instead they simply requested a specific treatment based on their personal
experience with breast cancer therapy. Perceived side effects and actual experience with
chemotherapy were also reported as influential factors in the treatment decisions by women
with breast cancer. Kreling and colleagues51 have found that women who underwent
chemotherapy experienced side effects that were more uncomfortable than what they had
expected with one subject who eventually requested a change in her chemotherapy regimen
due to the devastating effect of alopecia on her psychological health.
In this study, family opinions and opinions of others including a second physician
opinion have been considered by older myeloma patients as having an influence in their
treatment decisions. Other treatment decision making studies in patients with prostate,13
ovarian,43 breast,42, 51 and colorectal cancer44 have also found that these factors influence
treatment decisions.
The issues of therapy cost and its eventual impact on personal finances have been
described previously by Kutner et al.44 In this study, some patient participants related how
they made sure that their insurance covered the treatment they were going to have so as to
avoid any negative impact on their personal finances. Some of the participants took into
account the actual co-pays and out-of-pocket cost when they made their treatment decisions.
The physician’s expertise and practice type have been previously reported as having
an influence in treatment selection. For example, one study52 found that Hodgkin disease
experts are more likely to individualize patient’s therapy than non-Hodgkin expert physicians
and academic physicians are more likely to choose combined modality therapy (CMT) over
90
radiation therapy or chemotherapy alone. Similarly, in patients with LPC, a survey showed
that urologists tend to favor surgery while radiation oncologists tend to favor radiation
therapy over surgery.53 In an international survey, gastroenterologists tend to favor surgery
for the management of gastric lymphoma, while hematologists and oncologists are more
inclined to favor conservative therapy.54 In this study, the majority of physician participants
(N=8) worked in a myeloma practice with a strong stem cell transplant program, which in
effect had a clear influence on treatment decision making as far as screening patients for
autologous HSCT eligibility. This study showed that 9 out 10 physician participants screened
their patients for HSCT eligibility. There was only one physician participant who did not
mention screening for HSCT, a finding that could be explained by the context of the
physician interview, which unintentionally drifted towards the discussion of non-transplant
treatment options.
This small study was exploratory and, therefore, generalizability is inherently limited.
The study sample was primarily composed of Caucasian men and women with relatively
high incomes and high levels of education, and only the first 6 months after the diagnosis
were examined. Thus, the study findings may not be applicable to African American and
Hispanic patients or those patients who are beyond 6 months of diagnosis. In addition, the
findings may not be applicable to younger patients (<60 years of age) newly diagnosed with
myeloma, since they were not included in this study. This younger group may have a
different or differently weighted set of TDM influencing factors. In other words, younger
patients (≤60 years of age) newly diagnosed with myeloma may have a very different
perspective on treatment decision making than their older counterparts. Despite its
91
limitations, this study uncovers new personal and contextual factors influencing treatment
decisions. Further studies are recommended to validate these new findings.
Conclusion
Older myeloma patients (≥60 years of age) consider actual experience with myeloma
therapy, physician’s opinion, personal beliefs and values, family opinion, family burden,
social support, insurance and convenience of therapy as influential factors in their treatment
decisions. Physicians treating older patients with myeloma consider the patient’s
comorbidities, performance status, supportive care requirements, their own personal beliefs
and values, patient’s medical and clinical factors, patient’s context, family opinion and
patient’s treatment preference as having an influence on their treatment decisions. It is
critical that patients are given the opportunity to choose the best possible treatment for their
myeloma within the limits of their personal, social, and medical contexts.
92
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Hurria A, Leung D, Trainor K, et al. Factors influencing treatment patterns of breast
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Taylor WC, Muss HB. Adjuvant chemotherapy of breast cancer in the older patient.
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CHAPTER IV
Decisional Role Preferences in Older Adults Newly Diagnosed with Myeloma
Introduction
In the past three decades, treatment decision making (TDM) studies have focused on
decisional control preferences, with most studies conducted in breast and prostate cancer
samples. As reviewed by Tariman et al., these studies have shown an increasing ratio of
patients being interested in more active TDM participation.1 In the 1980s, decision-making
researchers examined decisional control preferences as a dichotomous variable rather a
continuous or ordinal variable.2-4 Through an extensive field study of how treatment
decisions are made for patients with life-threatening illnesses such as cancer, Degner and
Beaton5 uncovered 4 patterns of control over decision making.6 These observations led to the
formulation of the control preferences construct and the validation of the hypothesized
psychological dimension that patients have differing preferences about keeping control over
TDM, sharing control with their physician and relinquishing control to their physician.7
Early decision making studies from the late 1990s and early 2000s in older adults
with cancer showed that most preferred a specific option when presented with a variety of
treatment options for cancer8 and end-of-life preferences.9 Moreover, two systematic reviews
examining decisional control preferences have shown that patient preferences for
involvement in TDM are highly variable and that these decisional preferences change as the
disease progresses.1, 4 In order to meet and facilitate the patient’s preferred level of
participation during TDM, interventional studies geared toward increasing a patient’s
decisional satisfaction, reducing decisional conflict, and preventing anxiety and depression
99
related to TDM have been steadily increasing in numbers,10-26 with some studies targeting the
elderly cancer patient population.27-30
The growing interest in direct assessment of patient preferences for all components of
decision making,31-41 coupled with a predicted shift away from paternalistic decisions as baby
boomers age,41 present substantial opportunities for improving patient care and clinical
outcomes in the area of decision making. These opportunities are particularly true in the
older adult cancer patient population, which is often understudied or underrepresented in
clinical trials.
Increased patient participation in TDM has been associated with positive clinical
outcomes such as a greater level of patient satisfaction with decisions and better
psychological adjustment (e.g., less post-decision anxiety and depression) in patients.4, 42
However, the preferred level of participation in treatment decision making for patients
diagnosed with myeloma has not been previously studied. Moreover, the impact of older
adult patients’ participation in decision making processes is unknown, and the patient and
physician factors associated with participation in decision making specific to this population
have not been previously explored.
Theoretical Framework
Degner and Beaton’s Patterns of Decision Making5 provides the theoretical
framework for this study. These patterns encompass the various levels of patient
participation in decision making and are patient-centered, directly eliciting decisional role
preferences from the patient’s perspective. According to the model, four decision-making
patterns may occur. A provider-controlled decision-making pattern emerges when patients
decline to become involved in selecting their own treatment, even when urged to do so by
100
the physician. In this case, the patient is essentially saying: It’s up to you, doctor; you’re the
expert. On the other hand, a patient-controlled decision-making pattern occurs when patients
make it clear that they are the ones who will make the decisions.5 When patients want a
discussion of options with their physician and to think about the options prior to making the
final decision with their physician, a jointly-controlled decision-making pattern occurs.
Finally, when patients are incapable of making treatment decisions and the family makes
decisions for them, a family-controlled decision-making pattern emerges.5
Table 1 illustrates the various roles patients can play during treatment decision
making. This framework of decisional role patterns was developed based on a 4-year
qualitative study into decision-making roles in life-threatening situations such as cancer.5 The
control preference scale (CPS) is a measure of decision-making preferences7 and it identifies
these preferences on a series of cards.
Table 1. Degner and Beaton’s Patterns of Decision Making7
(The Control Preferences Scale)
_______________________________________________________________________
Active – Patient-Controlled
o I prefer to make the final treatment decision (Card A).
o I prefer to make the final treatment decision after seriously considering my doctor’s
opinion (Card B).
Collaborative – Jointly controlled
o I prefer that my doctor and I share responsibility for deciding which treatment is best
(Card C).
Passive – Provider-controlled
o I prefer my doctor to make the final treatment decision, but only after my doctor
have seriously considered my opinion (Card D).
o I prefer to leave all treatment decisions to my doctor (Card E).
Family-Controlled
o The family makes final decisions for the patient, who is incapacitated.
_______________________________________________________________________
101
Purpose
The purpose of the study was to describe the preferences for participation in decision
making of patients newly diagnosed with multiple myeloma and to explore the association
between sociodemographic variables and decisional role preferences.
Methods
Design and Sample
A cross-sectional survey approach was employed involving administration of the
Control Preferences Scale during a one-time semi-structured interview. The convenience
sample consisted of 20 older adults (60 years of age and above) referred to the Seattle
Cancer Care Alliance (SCCA) or the Northwestern University Myeloma Program (NUMP)
by hematologists/oncologists in the greater Seattle or Chicago areas, respectively. Eligibility
criteria included older adults who were (a) 60 years of age and above, (b) newly diagnosed
with multiple myeloma, (c) able to read and write English, and (d) able to give informed
consent.
Instrument
The CPS is a measure of decisional role preferences using a card-sort technique that
has two sets of five cards each. Each card describes a different role in decision making and
is illustrated with a sketch of characters (doctor/patient) representing their different roles in
decision making. The first set of five cards illustrates possible roles that the patient could
assume, ranging from the patient selecting his or her own treatment (cards A and B) through
a collaborative role model (card C) to a scenario where the physician makes the decision
(cards D and E) as shown in Appendix A. There were no cases of family controlled decisionmaking in the current study since the eligibility criteria excluded anyone in this category.
102
Patients were asked to complete the card sorts one at a time, comparing each card
with every other card in subsets of two, until their entire preference order across the set of
five cards was unfolded. The first two cards (B and D) were placed in front of the subject,
who was asked to select a preferred card based on a fixed-order presentation (B-D-C-E-A).
The preferred card was then placed on top of the non-preferred card. Then the next card (C)
was removed from the deck and placed beside the new stack of two cards. The participant
was asked to compare the card (C) to the most preferred card. If the patient still preferred the
previous card over the new one, the previous card was flipped over and the new card was
compared to the next one on a fixed-order basis as described above. Once the preferred card
was selected, the next card (E) was compared to the preferred card, and so on until the last
card (A) was compared to the previously preferred card. According to Degner et al., this
process unfolds the patient’s decisional role preference.7
The CPS offers an easy and quick way to elicit patient preferences about participating
in health care decision making.7 This scale has been found to be valid in the measurement of
decisional role preferences in patients newly diagnosed with various types of cancer,43
including men diagnosed with localized prostate cancer44, 45 and their partners,45 patients
diagnosed with colorectal cancer,46 breast cancer patients at any point of the disease
trajectory47 and older adults with breast cancer diagnosed in the past 18 months.48 Permission
to use the CPS scale for this study was obtained prior to study design.
Recruitment Procedures
After the researcher obtained approval from the University of Washington (UW) and
Northwestern University (NU) Human Subjects Divisions, older adults newly diagnosed with
myeloma were recruited to participate in the study. The researcher made every effort to
103
recruit from both university- and community-based practices in order to include a diversity of
study participants. Adults ages 60 and older who had an appointment at the hematology or
transplant services of SCCA and were found to be eligible for the study were recruited by
mail using a recruitment flyer. At NUMP, the researcher personally approached potential
participants (direct approach) and introduced the study to gauge their interest in participation.
A review of clinic schedules at SCCA and NUMP was conducted weekly to identify potential
study participants. One other UW-affiliated clinic (where myeloma patients were seen) was
checked weekly for potential study participants. To establish the decisional role preferences,
a card-sort technique was employed, using the fixed-order presentation described by Degner
and colleagues.7
In this study, the CPS was administered by the researcher during a semi-structured
interview conducted in designated research-related conference rooms at SCCA and NUMP.
These rooms were strictly assigned for research use only and met the human subject
division’s standard for patient privacy. If a patient wanted the interview to be conducted at a
later time, a one-week period following the appointment was allowed for re-scheduling.
Moreover, if a patient wanted the interview to be conducted at his/her home, the researcher
conducted the interview at the subject’s home, as requested.
Analysis
Coombs’ Unfolding Analysis
Data from the CPS scale were analyzed using the Statistical Analysis System® (Cary,
NC) computer program syntax developed by Sloan and Yeung.49, 50 This analysis was based
on a scaling model developed by Coombs, termed the unfolding theory.51 According to
104
Degner and colleagues7 this psychological scaling model is based on the assumption that “an
individual’s preference corresponds to an ideal point on a continuum, and that this ideal point
can be derived by presenting successive paired comparisons of stimuli that fall along the
continuum (p.25).” The unfolding theory holds that for any given hypothesized scale or
metric, only 11 subsets of the 120 possible permutations of the five decisional role cards will
be transitive. These 11 transitive responses range from ABCDE to EDCBA and they include
ABCDE, BACDE, BCADE, BCDAE, CBDAE, CDBAE, CDBEA, CDEBA, DCEBA,
DECBA and EDCBA. “Transitivity” means that an individual’s response falls within the 11
possible valid permutations and the individual’s preference for each of the paired
comparisons of stimuli is consistent with the hypothesized A to E psychological continuum.7
The reliability of the scale is established when 50% plus one of the experimental participants’
preferences were transitive, falling along the hypothesized scale (i.e., the ABCDE
psychological continuum).
Triangulation of Qualitative and Quantitative Data
Across-method triangulation,52 a form of methods triangulation was employed for
cross validation of the data pertaining to patients’ decisional role preferences. Patients’
verbal descriptions of their desired level of participation (as elicited from the interview) and
the patients’ preferences for participation (as measured by the CPS card sort) were compared.
This approach collected rich, detailed information from the participants regarding their
perspectives on decisional role preferences, which were then compared to the description of
the CPS cards.
Association Analyses
105
The differences in decisional role preferences were examined using dichotomous
categories of gender (male versus female), age (<70 years versus ≥70 years), education (less
than 4 years of college versus ≥4 years of college), relationship status (single versus
partnered), income (≤$55,000 versus >$55,000), and employment status (retired versus nonretired) were compared using simple analysis of variance (ANOVA). Spearman rank-order
correlations were used to determine the relationship between the ordinal CPS score and the
respondent’s age, education, and income variables. Given the results of the bivariate analysis
and the small sample size, multivariate analyses were not carried out.
Results
A total of seventy-nine potential participants was recruited by mail at SCCA from
October 2009 through July 2010. Of these, 14 (17.7 %) participants responded and all
participated in the study. At NUMP, the researcher identified six potential participants and
approached them about participation. All six potential participants agreed to participate.
Informed consent was obtained from all study participants before collecting any data.
Table 2 presents sociodemographic characteristics of the study participants. The
participants’ mean age was 67.5 years (age range: 60-82 years). The majority of participants
in this sample were Caucasian (90%); female (60%), married or partnered (60%), retired
(65%), had at least a 2-year college education (75%) and had an income >$35,000 (75%).
106
Table 2. Sociodemographic Characteristics of the Sample
Variable
Age (mean, 67.5 years)
60-70
70-82
Gender
Male
Female
Race
White
Asian
American Indian/Native
Alaskan Native
Employment Status
Full time
Working on medical leave
Not working
Retired
Student
Personal Relationship Status
Single
Married or partnered
Divorced
Widowed
Highest Level of Education
9th – 12th grade
2 years of college
4 years of college
Graduate degree
Annual Household Income
$18,000 or less
$18,000 to $35,000
$35,001 to $55,000
$55,001 to $85,000
$85,001 and above
N
%
14
6
70
30
8
12
40
60
18
1
1
90
5
5
2
2
2
13
1
10
10
10
65
5
2
12
5
1
10
60
25
5
5
2
10
3
25
10
50
15
3
2
5
5
5
15
10
25
25
25
Unfolding Analysis
As illustrated in Table 3, the hypothesized ABCDE metric has more than 50% of the
respondents’ answers corresponding to one of its 11 transitive preference orders. This means
that the data show support for an underlying dominant dimension of control, ranging from
107
keeping control (active - cards A and B) through collaboration (sharing- card C) to giving
away control (passive- cards D and E). The DCBAE metric also has more than 50% of the
respondents’ answers corresponding to one of its 11 transitive preference orders. This means
that a second, competing model of dichotomous preference (shared, card C, and active, cards
B and A) is seen in this group of older adults newly diagnosed with myeloma. The table also
shows the other competing scales under the column NAME (CBADE, ACBDE, etc.), which
did not meet the reliability criterion of 50% plus 1 of the experimental participants’
preferences corresponding to one of the 11 transitive preference orders. Table 3 summarizes
the participants’ valid responses (valid) and invalid response (invalid) and their
corresponding percentages (valper and invalper, respectively). There was no reversal found
since no participant selected an extreme role represented by card A or card E. Figure 1 shows
the distribution of respondents’ preferences falling on and off the transitive response of the
ABCDE metric.
Table 3. Rank Ordering of Competing Scale Models
SCALE
1
60
47
7
58
15
31
33
55
56
CONTROL PREFERENCES SCALING
SUMMARY FOR SCORING
NAME VALID VALPER INVALID INVALPER
ABCDE
DCBAE
CBADE
ACBDE
DBCAE
ADCBE
BCADE
BCDAE
DABCE
DACBE
14
14
9
7
7
6
6
6
5
4
70
70
45
35
35
30
30
30
25
20
6
6
11
13
13
14
14
14
15
16
30
30
55
65
65
70
70
70
75
80
EMCELL REVERSAL
3
6
8
8
8
8
9
8
9
10
N
N
N
N
N
N
N
N
N
N
108
Figure 1. Distribution of Respondents On and Off ABCDE Metric
Decisional Role Preferences
An examination of the distribution of preferences based on the first card in the
preference order indicated that 55% (N=11) of the participants preferred a shared role with
the physician and that 40% (N=8) preferred to make the decisions after seriously considering
the opinion of their physician (see Table 4). Only one participant preferred to relinquish the
decision to the doctor as long as the doctor considered the patient’s preferences. No
individual chose card A or E, which are the extremes of all the preference choices. Overall,
the percentage of the patients wanting to have some kind of control over the treatment
decision is very high at 95% (N=19).
109
Table 4. Distribution of Decisional Role Preferences
Preferred
Valid
Cumulative
Frequency Percent
Role
Percent
Percent
B (Active)
8
40.0
40.0
40.0
C (Shared)
11
55.0
55.0
95.0
D (Passive)
1
5.0
5.0
100.0
20
100.0
100.0
Total
Sociodemographic Variables and Decisional Role Preferences
There were no statistically significant differences in decisional role preferences across
dichotomous categories of gender (male versus female), age (<70 years versus ≥70 years),
education (less than 4 years of college versus greater than 4 years of college), partner status
(single versus partnered), income (≤$55,000 versus >$55,000), and employment status
(retired versus non-retired) using ANOVA. Given the small sample size, we explored only
simple bivariate differences between decisional role preference and these other factors. The
results of the bivariate analysis and the small sample size did not warrant further multivariate
analyses. There are also no statistically significant correlations between decisional role
preference and age, education, or income, respectively. The distribution of decisional role
preferences by age group, education, and income are shown in Table 5, 6, and 7,
respectively.
110
Table 5. Decisional Role Preferences by Age
Preferred Role
60-65
B (Active)
C (Shared)
D (Passive)
66-70
4
6
1
11
2
1
0
3
Age Group
71-75
76-80 81 and above
1
1
0
1
1
2
0
0
0
2
2
2
Total
8
11
1
20
Total
Table 6. Decisional Role Preferences by Education
Highest Level of Education
Preferred Role
9th to 12th
grade
B (Active)
C (Shared)
D (Passive)
Total
2-year college
2
3
0
5
4-year college
0
2
0
2
Graduate
degree
5
5
0
10
Total
1
1
1
3
8
11
1
20
Table 7. Decisional Role Preferences by Income
Annual Household Income
Preferred Role
$18,000 $18,000 to
or less $35,000
$35,001
$55,001 to $85,001
to
$85,000 and above
$55,000
Total
B (Active)
1
0
1
4
2
8
C (Shared)
2
2
4
1
2
11
D (Passive)
0
0
0
0
1
1
Total
3
2
5
5
5
20
111
Quantitative and Qualitative Decisional Role Preferences
Aside from using the CPS card-sort to measure the patients’ preferences for
decisional participation, patients were also asked to describe the decisional role that they
prefer from the CPS card. Table 8 illustrates the patient decisional role preferences using the
CPS card, the decisional category, and the patient’s own description of participation.
Table 8. Patient’s Decisional Role Preference, Category, and Description
Subject
ID
First
Card in
CPS
Decisional
Category
A
B
Active
“Well, we knew that it was
incurable, and the medical
community nationwide
seemed to be taking the
transplant approach to
improve the possibilities of
longevity. So we were
impressed with the facility
and all of the staff and have
decided to go ahead in that
direction.”
B
C
Shared
“I’d like to be well informed
of my choices, on both the
pros and cons of those
choices, in language I can
understand, so that I can help
participate in making the
choice.”
Patient’s Description of
Preferred Decisional Role
Description
Matches with
Decisional
Category
Yes
Yes
112
Table 8 continued
Subject
ID
First
Card in
CPS
Decisional
Category
Patient’s Description of
Preferred Decisional Role
C
C
Shared
“Well, I like to have the best
possible treatment plans to
cure my disease, and knowing
my doctor, I was confident at
that time to listen to her
opinion, and we made a
decision collectively to further
my treatment.”
D
C
Shared
“I would like to go over the
pros and cons of therapy. I see
them in my life for me and
then make that decision with
the doctor as far as what the
doctor feels after the doctor
heard where I was coming
from. The doctor knows what
would be the best for me in
this situation because the
doctor has the overall picture
and I only have pieces.”
Description
Matches with
Decisional
Category
Yes
Yes
113
Table 8 continued
Description
Matches with
Decisional
Category
No
Subject
ID
First
Card in
CPS
Decisional
Category
E
B
Active
“I want to know, I like to
understand, really, why
something is being done. Not
that I truly would understand
it as a lay person, but I want
to understand the logic for
doing something and I do
want to understand the
potential for being successful
and risk factors, you know,
because I want to use that to
gauge how fast I want to live
my life.”
G
B
Active
“So it was neutral at first, and
then as some of the shock
wore off and some of the
reality came in, I started
participating more in my
treatment.”
Yes
H
C
Shared
“The fact that before
treatments are started, I know
what it is going to be, and if
there is for some reason a
drug that I don’t feel I could
take, then I still have a right to
say no to that.”
Yes
Patient’s Description of
Preferred Decisional Role
114
Table 8 continued
Description
Matches with
Decisional
Category
Yes
Subject
ID
First
Card in
CPS
Decisional
Category
Patient’s Description of
Preferred Decisional Role
I
D
Passive
“I definitely want to be
involved in the decision, but
knowing that the doctor
knows more than I do about
the treatments that are
available and which one I’m
best suited for, I would go
with the doctor’s opinion after
I’ve heard what the options
are and discussed them.”
J
B
Active
“I would like to have full
involvement. I will listen to
what the doctor says or what
he feels, because I feel he has
that knowledge. And I
probably would take his
recommendation, but I would
make the decision myself.”
Yes
K
C
Shared
“My oncologist seems to think
that acupuncture and massage
are all fine, but those are
things that I’ve explored
myself. So I take his advice,
and then I do my other kinds
of things that are alternative
sorts of things, too.”
Yes
115
Table 8 continued
Description
Matches with
Decisional
Category
Yes
Subject
ID
First
Card in
CPS
Decisional
Category
L
B
Active
“I write things down and I try
to get as many opinions as
possible. And of course I
think the doctor’s opinion
about what to do is 90% of
what it is. But I want to
understand, if decisions are
being made on my behalf,
why they're being made.”
M
C
Shared
“Well, my preference is and it
has been so far is shared
involvement with the doctor
and also a key to it has been
the ability to be able to get a
second opinion, so I can have
two experts look at the
situation.”
Yes
N
B
Active
“My wife was heavily
involved in making the
decision, too. Since she was
very much affected by it, so
together we made the
decision. I probably had a
little more influence than her,
but her opinion was
considered as well.”
Yes
Patient’s Description of
Preferred Decisional Role
116
Table 8 continued
Description
Matches with
Decisional
Category
No
Subject
ID
First
Card in
CPS
Decisional
Category
O
C
Shared
“I want to know. I am very,
very nosey that way. I want to
know.”
P
C
Shared
“I want her [my doctor] to see
how I’m doing and see what
condition I am and how I’m
progressing, you know; worse
or better, then, make her
decision based on that.”
No
Q
C
Shared
“I take the input from what
I've gotten back from the tests
that my doctor sends me. But
that wasn't good enough for
me because I wanted a second
opinion.”
Yes
R
C
Shared
“Now that I’ve kind of had a
chance to step back and have
a more sober, view of it and
more objective view, I feel
that I’m in a better frame of
mind, if you would, to maybe
look at the options and discuss
this in a more objective
manner.”
Yes
Patient’s Description of
Preferred Decisional Role
117
Table 8 continued
Description
Matches with
Decisional
Category
Yes
Subject
ID
First
Card in
CPS
Decisional
Category
S
C
Shared
“I want to know about things.
I'm curious. I want to know as
much as I can. And then with
the help of my husband and
my kids, make a decision.”
T
B
Active
“Well, I will make my
decision along with my
husband at that point on
which is best for us as a
family, and we rely upon our
doctor's medical advice to
lead us to a conclusion.”
Yes
U
B
Active
“I ask her [my doctor]
everything I can think of
when we meet. I listen to what
she has to tell me. If the
decision is something clear
enough that I can make it
immediately or if I need to
make it immediately, I do.”
Yes
Patient’s Description of
Preferred Decisional Role
Note: Study ID numbers have been changed to ensure full protection of participants
confidentiality.
Discussion
In this study of decisional role preferences in older adults newly diagnosed with
myeloma, a high percentage (95%) of participants wanted to have some control (shared and
active roles) of the treatment decisions with only 1 subject (5%) expressing preference for a
passive role. This finding is contrary to previous reports that older adults are passive
recipients of medical care.43, 53-57 Perhaps the impact of advanced age on relinquishing
118
decisional control to physicians is moderated by the higher education and higher income
profile of the study participants; these are variables previously reported as having strong
correlations with more decisional control.43, 48, 58, 59 Alternatively, myeloma patients may
have a different profile of decisional role preferences from patients with other cancers.
The findings of the study are consistent with the most recent findings reported in
studies from the United Kingdom60 and Canada,25, 44 where research showed increasing
numbers of patients wanting to have some control of treatment decisions—as high as 92-93%
in some studies. It would be interesting to see a consistent trend of patient’s decisional role
preferences moving towards more participation in the near future.
Based on interview transcripts from the current study, it appears that patients’
preferred decisional roles elicited from the use of the CPS scale generally matched with their
individual descriptions of decisional role preference. This finding supports the hypothesized
construct that patients have various decisional preferences about keeping, sharing, or
relinquishing control over treatment decisions. It should be noted, however, that we did not
measure the degree of congruence between patients’ desired role and actual role during the
TDM process.
Participants in this study demonstrated a strong desire to participate in the decisionmaking process, though they may not have had a full understanding of myeloma and its
treatment options. It is possible that the participants have unmet information needs leading to
more active participation. In their descriptions of role preferences, participants in this study
reported seeking information from various sources and some participants identified their
doctors as the primary source to explain the different treatment options available to them.
119
This finding has strong implications for physicians regarding how they provide information
to patients.
The emergence of a second valid metric in Coombs’ unfolding analysis warrants
further exploration. The participants had a nearly 50/50 distribution between shared and
active decisional role preferences. Only 1 subject chose to be a passive recipient of care. This
is clearly a trend seen in the Western societies, where health care consumerism is on the
rise.61-63 One could theorize that the former paternalistic model of physician-patient
relationship is losing ground, as suggested by Rosenstein in the mid-1980s.64 Patient
preferences have always tended to fall into a dichotomy of preferences. The shift in
decisional preference toward either a shared or active role for patients has not been reported
before. In the past, patient preferences tended to fall into either the “active” or “passive”
category.2, 3 It is also possible that the majority of the participants in this study included
patients who are not too sick from the myeloma or from their chemotherapy, and therefore
allows them to be more active with their treatment decisions.
There are limitations of this study primarily related to sample size and demographics.
The small sample consisted of mostly Caucasians who were relatively highly educated and
high-income individuals. In addition, the majority of participants (85%) were receiving care
at a university-based comprehensive cancer center. The small sample size and lack of a
diverse sample limit the generalizability of study findings. We were unable to make more
meaningful comparisons of differences in decisional role preferences using dichotomous
sociodemographic variables and were also unable to examine associations of decisional role
preferences based on specific combinations of sociodemographic variables using multivariate
analysis. Moreover, since this is a cross-sectional study, the findings may not be applicable to
120
myeloma patients who are beyond 6 months of diagnosis. These limitations should be
addressed in future research. Additionally, further study using a longitudinal approach is
needed to better describe the stability or change in patients’ decisional role preference over
time.
Implications for Practice
The study findings suggest that patients diagnosed with myeloma do want to
participate in the treatment decision-making process. Although myeloma is complex and not
easy to understand for laypersons, these findings indicate that patients still want to learn as
much as they reasonably can about the disease and treatment so as to understand the reason
why certain treatment options might be better for them than others. The majority of
participants also wanted to share the treatment decision with their physicians and/or want to
make the decision themselves. Therefore, it is critical for physicians to practice full
disclosure of treatment options to their patients so patients can make a truly informed
decision. Since a patient’s level of preference for participation is highly variable and is
unique in terms of personal meaning, physicians need to elicit the patient’s preference,
explore what participation truly means for him/her and facilitate the patient’s decision
process. Because more patients now want to participate in TDM, physicians, nurses, and
policy makers should support more research that can enhance patient involvement in
treatment decision making.
Conclusion
Older adults newly diagnosed with myeloma are likely to want a role in the treatment
decision-making process. Nurses can help patients achieve the level of participation they
desire by making sure patients receive disease and treatment-related information and
121
encouraging patients to express their decisional role preference to the physician. Nurses can
also convey the patient’s decisional role preferences to the physician. More studies that focus
on supporting and involving patients in the decision-making process are needed in order to
influence clinical practice and policy in this direction.
122
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130
CHAPTER V
The Priority of Information Needs of Older Adults Newly Diagnosed with Myeloma
Introduction
Providing pertinent information to patients with cancer with which to understand the
diagnosis and its consequences and make treatment and self-care decisions is considered an
important part of standard care.1 Evaluating which information needs are of highest priority
has been studied in breast, prostate, and other cancer patient populations;2 however, cancer
patients continue to have unmet information needs, characterized by different information
needs and information-seeking behaviors among patients with different types of cancer.3 In a
seminal paper, Degner and colleagues4 argued that in an era of scarce health care resources,
patient information needs are best prioritized. Prioritization directs the attention of nurses to
the most important information needs, enhances the delivery of information that patients
need, and provides relevant information to patients at specific periods of their illness.
Additionally, prioritization of information needs can make patient encounters more
meaningful. As reviewed by Husson and colleagues,5 when information is individualized to
fulfill what a patient deems important, the anxiety and depression associated with treatment
decision making may be lower.
Nurses play a critical role in giving patients the information they need and can
potentially help patients assume a more active role in decision making when desired or feel
more comfortable when decisions are left to the provider. Conversely, a lack of information
can hinder patients’ participation in treatment decision making or increase uncertainty.
Patients often report difficulties obtaining the type and amount of information they want or
need.6-8 Oncology nurses are poised to provide useful, concise, and relevant information, but
131
they have significant time constraints to provide full information that most cancer patients
need.9 By determining the particular types of information that patients view as important,
nurses can potentially close the gap between what patients want to know and what nurses
believe patients need to know.
A new diagnosis of myeloma involves a wide choice of drugs and treatment
regimens, including thalidomide, bortezomib, lenalidomide, and liposomal doxorubicin.10, 11
Advances in hematopoietic stem cell transplantation also have increased the treatment
options for patients with myeloma.12, 13 Moreover, patients have many information needs, not
only in terms of their treatment choices, but in terms of treatment side effects, supportive
care, and overall quality of life. Older adults with cancer have various information needs that
are oftentimes not met by clinicians, which may be different than the information needs of
their younger counterparts.14 Studies of information needs priorities involving older adults
revealed a different priority of information needs profile than their younger counterparts.15-18
Since myeloma frequently affects older adults, studying the priority of information needs of
these patients could reveal a different priority of information needs than for younger
myeloma patients.
Purpose
The purpose of this study was to examine the priority information needs of a sample
of older adults (60 years and older) newly diagnosed with myeloma and to explore whether
information needs were influenced by sociodemographic variables such as age, gender,
education, relationship status, and income.
132
Methods
Design and Sample
A cross-sectional survey approach was employed involving administration of an
information needs questionnaire (INQ) with 36 paired comparisons during a one-time semistructured interview. The convenience sample consisted of 20 older adults referred through
the Seattle Cancer Care Alliance (SCCA) or the Northwestern University Myeloma Program
(NUMP) by hematologists/oncologists in the greater Seattle or Chicago areas, respectively.
Eligibility criteria included older adults 60 years of age and above who were (a) newly
diagnosed with multiple myeloma, (b) able to read and write English, and (c) able to give
informed consent.
Procedures
After obtaining approval from the University of Washington (UW) and Northwestern
University (NU) Human Subjects Divisions, older adults newly diagnosed with myeloma
were recruited to participate in the study. The researcher made every attempt to recruit from
both university- and community-based medical practices in order to recruit diverse study
participants. Adults ages 60 and older who had an appointment at SCCA hematology or
transplant service and were found to be eligible for the study were recruited by mail using a
recruitment flyer. At NUMP, the researcher utilized a direct approach in the recruitment of
study participants. A review of clinic schedules at SCCA and NUMP was performed weekly
to identify potential study participants. UW-affiliated clinics were also checked weekly for
potential study participants. A total of 79 potential participants were recruited by mail at
SCCA from October 2009 through July 2010. Fourteen participants responded and
participated in the study (a 17.7 % response rate). At NUMP, all six potential participants
133
agreed to participate (a 100% response rate). Informed consent was obtained from all study
participants.
The Information Needs Questionnaire (INQ)4 was administered by the researcher
during a semi-structured interview conducted in designated research-related conference
rooms at SCCA and NUMP. These rooms were strictly assigned for research use only and
met the Human Subject Division’s standard for patient privacy. If a participant wanted the
interview to be conducted at a later time, a one-week period following the appointment was
allowed for re-scheduling. Moreover, if a participant wanted the interview to be conducted at
his/her home, the researcher conducted the interview at the participant’s home as requested.
The participants were informed they could end the interview at any time and refuse to answer
specific questions.
Instrument
The INQ was initially developed by Degner and colleagues,3 applied first in studies of
women with breast cancer15, 19 and extended by Davison in her work with men diagnosed
with prostate cancer.20, 21 The questionnaire has been found to be valid and reliable in
determining the priorities of information needs of patients diagnosed with various types of
cancer.4, 15, 17, 19, 20, 22-24 The INQ contains nine categories of information topics each with a
statement of definition: (a) prognosis [likelihood of cure], (b) stage of disease [spread and
extent of cancer], (c) side effects [possible side effects of treatment], (d) treatment options
[treatment available], (e) social activities [impact on work, daily activities, and social life],
(f) family risk [hereditary risk], (g) home self-care [health care needs during and following
treatment], (h) impact on family [helping family members deal with cancer diagnosis], and
134
(i) sexuality [treatment options and counseling for sexual concerns] (See Table 1). The
Thurstone25 scaling method is the traditional analytic approach for the INQ.4
Table 1. The Nine Items of Information Represented in the Information Needs
Questionnaire (INQ), Adapted for Myeloma
1 Information about how advanced the disease is and how far it has spread
2 Information about the likelihood of being cured of myeloma
3 Information about how the treatment may affect my ability to carry on my usual social
activities
4 Information about how my family and close friends may be affected by myeloma
5 Information about caring for myself at home
6 Information about how the treatment may affect my feelings about my body and sexual
attractiveness
7 Information about different types of treatment and the advantages and disadvantages of
each treatment
8 Information about whether my children or other members of my family are at risk of
getting myeloma
9 Information about unpleasant side effects of treatment
Analysis and Instrument Performance
Scaling Techniques - Thurstone Scaling Method
There are two main types of measurement scales that have been used in cancer
research: summated and differential. Summated scales include scales that use a Likert-type
scale such as 1 (completely agree) to 12 (completely disagree).26 Studies that use summated
scales have demonstrated problems with a “ceiling effect,” where the studied group tends to
rate items close to the upper limit of the scale for each category, ranking any information
topic as low that may not be socially desirable.4 Ceiling effect can also occur if the study
participants have very little variability in the psychological attribute being measured. The
ceiling effect has been demonstrated in studies conducted by Davison et al.20 and Degner and
colleagues4, 15 when utilizing summated scales. Summated scales have also been criticized for
reliability and validity problems due to lack of approximation of equal units and meaningful
135
order, both of which are key aspects of interval-level measurement.27 As suggested by
Degner and colleagues,4 when ceiling effects are a common occurrence, an alternative
scaling model is needed, so the Thurstone Scale Case V Model - the standard approach
among five cases in Thurstone’s model of comparative judgment25 - was used in this study.
In 1927, Thurstone28 postulated that each component in a set of stimuli, such as the
nine items of the INQ, will possess some attribute (e.g., a particular information item will
have a lower or higher value of priority, but the expected level of priority is unknown).
Thurstone’s Law of Comparative Judgment would predict that respondents will “prefer one
item over other items” (p.60)29 for each of the nine items derived from the INQ. Additionally,
the law assumes that the differences in the standard deviation (i.e., preference proportions) of
all the participants’ responses to each item in the scale would be equal. The more participants
select one item of a pair over the other item, the greater the preference for, or perceived
priority of, that item and thus the greater its scale weight. The scale weight was calculated by
using the actual percentage of preferred items and was then used to rank the nine items of the
INQ. Detailed descriptions of the methods used to execute Thurstone’s paired comparison
can be obtained from Sloan and colleagues.29
The INQ was selected for the current study because it has been piloted in several
patient samples in cancer settings and shown to capture the information needs that patients
consider priorities. These items were checked for simplicity, avoiding confusion among
participants, and meeting the requirement of easy interpretability. For Thurstone’s paired
comparison scaling to be carried out, it has been suggested to compare a few items.30 The
INQ had nine information categories, which was the maximum number of items that would
still avoid subject fatigue. With nine items in the INQ, 36 pairings are required to obtain
136
every possible pairing. To avoid selection bias, the Ross (1974)31 matrix of optimal ordering
was used to determine the exact same order in which the 36 paired items would be presented
to all study participants. This method of ordering ensures maximum time and space
distancing among all the possible 36 pairs of information items.
For this study, data from the INQ were analyzed using the traditional Thurstone
scaling, as entered into the Predictive Analytic SoftWare (PASW) Statistics version 18
(Armonk, NY). Preference proportions were calculated, then translated into standard normal
scores (z-scores), representing the participant’s perceived priorities of the information items.
A score below zero meant that the number of participants who considered the information
item as a priority was less than half of the total number of participants. The information item
that had the highest score was prioritized by the majority of the participants. When the zscore is at 0, it meant that the prioritization of that particular information item was ranked by
half of the participants.
Validity of INQ
The validity of the INQ was tested by examining differences in Thurstone scores.
Testing of the validity of the INQ is critical to determine whether or not the observed
differences between the scale scores for individual items were merely sampling fluctuation
caused by chance or a significant difference in terms of level of priority.29 Multiple t-tests
were used to test for significant differences between Thurstone scale scores across the
respondents. A standard error was calculated from the individual normal deviations that
produce the Thurstone scores. A Bonferroni correction to the p-value of each participant was
applied to correct for multiple t-tests conducted across all participants’ Thurstone scale
scores.29
137
Reliability of INQ
The reliability of the INQ was determined by computing the response
variability/scalability of the scores and by checking internal model consistency.
Response Variability/Scalability of Score. As recommended by the primary authors of
the INQ, Gulliksen and Tukey,32 a reliability statistic comparable to the R-square statistic
was used to describe the reliability of participants’ individual responses to the information
needs questionnaire. Gulliksen and Tukey's statistic measures the extent to which the
Thurstone scale scores adequately explains the differences in the individual’s actual values
from the expected values, utilizing analysis of variance theory.29 Similar to the R-square
statistic, the greater the Gulliksen and Tukey R2 statistic, the greater the scalability of the
data.4 Our results indicate that the participants’ individual responses to the nine items in the
INQ were moderately reliable as indicated by the Gulliksen and Tukey reliability measure
value (R2 = .742) as shown in Table 2.
Internal Rater Consistency. The internal rater consistency was established by
calculating the circular triads (see below), Kendall’s consistency coefficient (zeta) and
Kendall’s coefficient of agreement. Mosteller’s chi-square test of internal consistency33 was
used to measure internal model consistency. Mosteller’s chi-square test is a goodness-of-fit
test that checks the adequacy of the model fit for Thurstone scaling models (in this study,
Thurstone’s Case V model). The assumptions for the Thurstone Case V model in this study
are as follows:25, 29, 34

The value of the psychological attribute (i.e., priority for the INQ) has a normal
distribution

Measurement of only one attribute (i.e., priority)
138

The differences between the expected and actual values (standard deviations) of
the psychological attribute on any given paired items of the INQ are equal
The Mosteller’s chi-square test and the p-value are used to determine how the model
fits the data. The null hypothesis is that the resulting scale values are non-significant, or in
other words, that the expected scale values fit the observed data.29 The Mosteller’s chi-square
for the present study was 39.39 (p = .075) with 28 degrees of freedom. The null hypothesis
is not rejected, indicating that the Case V model fits the data (See Table 2), and therefore it is
reasonable to conclude there is demonstrable consistency between the model and the data.
Table 2. Gulliksen & Tukey and Mosteller’s Chi-square for INQ for Myeloma
Gulliksen &
Tukey
R-square (%
of total
Mosteller
variance
chi-square
accounted
statistic
Mosteller
for by scale for internal
Degrees of Mosteller N of
scores)
consistency freedom
p-value
raters
.74175
39.38729
28.00
.07489
20
Circular triads. Internal consistency of an individual’s comparative judgment is
assessed by computing the number of circular triads present in the comparisons.29 By
calculating circular triads, the consistency of participants in selecting the information
category which they considered a priority is established. A participant is said to have a
circular triad when a participant is not consistent with his or her comparative judgment. As
explained by Edwards (1974),35 when a participant is presented with all three possible pairs
of items 1, 2, and 3 from the INQ, the participant must prioritize item 1 over item 2, item 2
139
over item 3 and item 1 over item 3. In this specific example, if the participant prioritizes item
3 over item 1, a circular triad has occurred.
In the present study, two of the twenty (10%) participants were always consistent in
their choices, with no circular triads, demonstrating complete consistency in their judgment
of which information category was more important. Only one participant had 24 circular
triads, which could mean that the participant was confused about the items, bored from
answering the 36 item questionnaire, or had difficulties in distinguishing one item from
another. Overall, the total number of circular triads made by all participants appears to
support a moderately strong internal rater consistency among the participants’ comparative
judgments. The range of circular triads for this study was 0-24 as shown in Table 3.
140
TABLE 3. Circular Triads and Kendall’s (zeta) Consistency Coefficient
Number
of circular
triads
per
person
Maximum
possible
n of
triads
Critical
value
for n
of triads
Kendall
zeta
consistency
Degrees
coeffiof
cient
freedom
Chi
square
P
value
for
Kendall
zeta
Consistency
of
item
rating
______
______
______
______
______
______
______
______
12.00
7.00
4.00
.00
11.00
14.00
24.00
9.00
4.00
4.00
7.00
14.00
1.00
7.00
11.00
8.00
12.00
5.00
1.00
.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
30.00
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
27.24
.6000
.7667
.8667
1.0000
.6333
.5333
.2000
.7000
.8667
.8667
.7667
.5333
.9667
.7667
.6333
.7333
.6000
.8333
.9667
1.0000
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
20.16
35.36
43.36
48.16
54.56
36.96
32.16
16.16
40.16
48.16
48.16
43.36
32.16
52.96
43.36
36.96
41.76
35.36
46.56
52.96
54.56
.9807
.9980
.9996
.9999
.9874
.9562
.2843
.9949
.9996
.9996
.9980
.9562
.9999
.9980
.9874
.9968
.9807
.9993
.9999
.9999
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
Kendall’s zeta coefficient (range 0 to 1) was used to test the null hypothesis that the
presence of triads was due to chance or indicative of inconsistent logic. The closer the
coefficient is to 1, the more consistent the participants are in their comparative judgments of
the preferred items. In this study the test for Kendall’s zeta coefficient showed that the
majority of the participants (90%) were consistent with their comparative judgments of the
preferred items. According to Sloan and colleagues,29 a limitation of this statistic is that it is
quite insensitive, so a large number of circular triads (i.e. ≥27 circular triads) would have to
be present for the responses to be identified as inconsistent. The possibility of such an
141
occurrence was seen in only 1 (5%) out of the twenty participants; this participant had 24
circular triads and was nearing the critical value of 27 circular triads.
Kendall’s coefficient of agreement was used to determine the extent of agreement
among participants with reference to their comparative judgment of the 36 paired
comparisons found in the INQ. When the coefficient is higher (0 to 1), it means there is
higher agreement among participants regarding their overall priorities for the nine items in
the INQ. Sloan and colleagues29 emphasized that this test has a broad null hypothesis (i.e.,
departure from random preference rating), frequently resulting in a statistically significant
test even though the level of agreement among participants could still be low. The present
study showed that the agreement among myeloma patients in their paired comparative
judgment is low but it is still statistically significant (W=.25; p<0.001) as seen in Table 4.
Table 4. Kendall’s Coefficient of Agreement
Number
of
raters
Number
of
items
Kendall
Coefficient
of
agreement
Chi-square
Degrees
of
freedom
p-value
indicates
agreement
among
raters
_______
_______
_______
________
_______
________
20
9
.25526
236.22222
42.22
.001
___________________________________________________________________________
Association of Sociodemographic Variables with Information Priorities
Differences in information priorities across dichotomous categories of age (>70 years
versus ≤70 years), education (less than 4 years of college versus greater than 4 years of
college), relationship status (single versus partnered), income (<$55,000 versus ≥$55,000),
and employment status (retired versus non-retired) were compared using Student’s t-tests.
142
Given the results of the bivariate analysis and the small sample size, multivariate analyses are
unwarranted.
Results
Table 5 presents the sociodemographic characteristics of the 20 study participants.
Participants’ mean age was 67.5 years; median 62.5 years. The majority of participants in
this sample were Caucasian (90%), female (60%), partnered (60%), retired (65%) and had at
least a 2-year college education (75%); half the sample reported an annual household income
>$55,000
Table 5. Sociodemographic Characteristics of the Sample
Variable
Age
60-70
71-82
Gender
Male
Female
Race
Caucasian
Asian
American Indian/Native
Alaskan
Employment Status
Full time
Working on medical leave
Not working
Retired
Student
N
%
14
6
70
30
8
12
40
60
18
1
1
90
5
5
2
2
2
13
1
10
10
10
65
5
143
Table 5 continued
Variable
Personal Relationship
Status
Single
Married or partnered
Divorced
Widowed
Highest Level of
Education
9th – 12th grade
2 year college
4 year college
Graduate degree
Annual Household
Income
$18,000 or less
$18,000 to $35,000
$35,001 to $55,000
$55,001 to $85,000
$85,001 and above
N
%
2
12
5
1
10
60
25
5
5
2
10
3
25
10
50
15
3
2
5
5
5
15
10
25
25
25
Preference Frequencies
The number of participants who preferred one item over another for all the
comparisons is shown in Table 6. Using the preference frequencies, the preference proportion
was obtained by dividing the preference percentage by the total number of respondents. The
more participants that select one item of a pair over the other item, the greater the preference
for, or perceived priority of, that item, and thus the greater its scale weight. The scale weight
was calculated by using the actual percentage of preferred items and was then used to rank
the nine items of the INQ.
144
Table 6. Frequencies of Participants’ Preference for 36 Paired Items in the INQ
Comparison
Presented
Item Preference
Item 1 versus Item 2
Second item preferred
First item preferred
Total
Response
Yes
13
0
13
Total
Responses
13
7
20
10
0
10
10
10
20
.00
0
1.00
8
Total
8
12
12
0
8
12
20
.00
0
1.00
11
Total
11
9
9
0
11
9
20
.00
0
1.00
2
Total
2
18
18
0
2
18
20
.00
0
1.00
18
Total
18
2
2
0
18
2
20
No
0
7
7
I31
Item 1 versus Item 3
Second item preferred
First item preferred
Total
0
10
10
I41
Comparison of Item 1
versus Item 4
Total
Second item
preferred
First item preferred
I51
Comparison of Item 5
versus Item 1
Total
Second item
preferred
First item preferred
I61
Comparison of Item 6
versus Item 1
Total
Second item
preferred
First item preferred
I71
Comparison of Item 7
versus Item 1
Total
Second item
preferred
First item preferred
145
Table 6 continued
Comparison Presented
Item Preference
Response
Yes
Total
Responses
.00
0
1.00
8
Total
8
12
12
0
8
12
20
.00
0
1.00
11
Total
11
9
9
0
11
9
20
.00
0
1.00
8
Total
8
12
12
0
8
12
20
.00
0
1.00
7
Total
7
13
13
0
7
13
20
.00
0
1.00
8
Total
8
12
12
0
8
12
20
.00
0
1.00
16
Total
16
4
4
0
16
4
20
No
I81
Comparison of Item 8
versus Item 1
Total
Second item
preferred
First item preferred
I91
Comparison of Item 9
versus Item 1
Total
Second item
preferred
First item preferred
I23
Comparison of Item 2
versus Item 3
Total
Second item
preferred
First item preferred
I42
Comparison of Item 4
versus Item 2
Total
Second item
preferred
First item preferred
I25
Comparison of Item 2
versus Item 5
Total
Second item
preferred
First item preferred
I62
Comparison of Item 6
versus Item 2
Total
Second item
preferred
First item preferred
146
Table 6 continued
Comparison Presented
Item Preference
Response
No
Yes
Total
Responses
I27
Comparison of Item 2
versus Item 7
Total
Second item
preferred
First item preferred
.00
0
1.00
11
Total
11
9
9
0
11
9
20
.00
0
1.00
13
Total
13
7
7
0
13
7
20
.00
0
1.00
6
Total
6
14
14
0
6
14
20
.00
0
1.00
6
Total
6
14
14
0
6
14
20
.00
0
1.00
5
Total
5
15
15
0
5
15
20
.00
0
1.00
3
Total
3
17
17
0
3
17
20
I82
Comparison of Item 8
versus Item 2
Total
Second item
preferred
First item preferred
I29
Comparison of Item 2
versus Item 9
Total
Second item
preferred
First item preferred
I34
Comparison of Item 3
versus Item 4
Total
Second item
preferred
First item preferred
I53
Comparison of Item 5
versus Item 3
Total
Second item
preferred
First item preferred
I36
Comparison of Item 3
versus Item 6
Total
Second item
preferred
First item preferred
147
Table 6 continued
Comparison Presented
Item Preference
Response
Yes
Total
Responses
.00
0
1.00
0
Total
0
20
20
0
0
20
20
.00
0
1.00
11
Total
11
9
9
0
11
9
20
.00
0
1.00
6
Total
6
14
14
0
6
14
20
.00
0
1.00
14
Total
14
6
6
0
14
6
20
.00
0
1.00
18
Total
18
2
2
0
18
2
20
.00
0
1.00
19
Total
19
1
1
0
19
1
20
No
I73
Comparison of Item 7
versus Item 3
Total
Second item
preferred
First item preferred
I38
Comparison of Item 3
versus Item 8
Total
Second item
preferred
First item preferred
I39
Comparison of Item 3
versus Item 9
Total
Second item
preferred
First item preferred
I45
Comparison of Item 4
versus Item 5
Total
Second item
preferred
First item preferred
I64
Comparison of Item 6
versus Item 4
Total
Second item
preferred
First item preferred
I47
Comparison of Item 4
versus Item 7
Total
Second item
preferred
First item preferred
148
Table 6 continued
Comparison Presented
Item Preference
Response
No
Yes
Total
Responses
I48
Comparison of Item 4
versus Item 8
Total
Second item
preferred
First item preferred
.00
0
1.00
12
Total
12
8
8
0
12
8
20
.00
0
1.00
8
Total
8
12
12
0
8
12
20
.00
0
1.00
0
Total
0
20
20
0
0
20
20
.00
0
1.00
16
Total
16
4
4
0
16
4
20
.00
0
1.00
10
Total
10
10
10
0
10
10
20
.00
0
1.00
10
Total
10
10
10
0
10
10
20
I94
Comparison of Item 9
versus Item 4
Total
Second item
preferred
First item preferred
I56
Comparison of Item 5
versus Item 6
Total
Second item
preferred
First item preferred
I57
Comparison of Item 5
versus Item 7
Total
Second item
preferred
First item preferred
I85
Comparison of Item 8
versus Item 5
Total
Second item
preferred
First item preferred
I59
Comparison of Item 5
versus Item 9
Total
Second item
preferred
First item preferred
149
Table 6 continued
Comparison Presented
Item Preference
Response
Yes
Total
Responses
.00
0
1.00
1
Total
1
19
19
0
1
19
20
.00
0
1.00
17
Total
17
3
3
0
17
3
20
.00
0
1.00
2
Total
2
18
18
0
2
18
20
.00
0
1.00
16
Total
16
4
4
0
16
4
20
.00
0
1.00
4
Total
4
16
16
0
4
16
20
.00
0
1.00
3
Total
3
17
17
0
3
17
20
No
I76
Comparison of Item 7
versus Item 6
Total
Second item
preferred
First item preferred
I68
Comparison of Item 6
versus Item 8
Total
Second item
preferred
First item preferred
I96
Comparison of Item 9
versus Item 6
Total
Second item
preferred
First item preferred
I87
Comparison of Item 8
versus Item 7
Total
Second item
preferred
First item preferred
I79
Comparison of Item 7
versus Item 9
Total
Second item
preferred
First item preferred
I98
Comparison of Item 9
versus Item 8
Total
Second item
preferred
First item preferred
Priorities of Information Needs (Rank Ordering of Nine Information Items)
150
The Thurstone scale analysis for 20 participants newly diagnosed with myeloma
indicated that the top three priority information needs were related to: the “different types of
treatments” and corresponding advantages/disadvantages, the “likelihood of cure” and
“caring for myself at home.” The item about the impact of treatment on “feelings about my
body and sexual attractiveness” was ranked in last place (See Figure 1).
When asked if there were any other items of information that were important but had
not been included in the INQ, two individuals in this study gave an affirmative response.
These comments by two participants included information needs related to stem cell
transplantation (What is the length of hospital stay?) and overall survival (How long will I
live with myeloma?).
151
Figure 1. Priority of information needs by rank. The items on the left are ranked 1-9 starting
from the top to bottom. The items on the right are the Case V scores.
Sociodemographic Variables
Analysis of information needs by subgroup revealed no statistically significant
differences across dichotomous categories of age (>70 years versus ≤70 years), education (<4
years of college versus ≥years of college), partner status (single versus partnered), income
(<$55,000 versus ≥$55,000), or employment status (retired versus non-retired).
Discussion
These results suggest that older adults newly diagnosed with myeloma have a profile
of information needs priorities that clinicians can use to initiate information exchange. The
profiles suggest that older adults newly diagnosed with myeloma want to discuss the
152
treatment options available to them as well as how these options may impact myeloma
outcomes. Since the outcome of myeloma is not heavily influenced by the stage of disease at
the time of diagnosis compared to other malignancies, this could explain why the study
participants ranked disease stage as the fifth priority out of nine priorities.
In contrast to younger cancer patients, who have been shown in several studies to
have prognosis, disease stage and treatment options as their top three priorities,15-17 this study
demonstrated that the priority of information needs for older patients was information about
treatment options, likelihood of cure and self-care. Older adults value independence;36
hence, it was not surprising to find that self-care was one of the top three priority information
needs. This finding is supported by another study, which found that older breast cancer
patients (65 and above years of age) ranked self-care information more important than did
younger patients.19 Another study involving older participants diagnosed with esophageal
cancer in Sweden (58-86 years old, with a mean of 69 years of age) also found treatment and
self-care in the top three priorities of information needs, and sexual attractiveness was ranked
as the last priority.37 In contrast to the current study, another study that involved younger
breast cancer patients in Malaysia (32-60 years of age, with a mean of 45.09 years of age)
found that sexual attractiveness was ranked as the second top priority information need by
the participants.38
The rankings in this study must be interpreted with caution, because a single
assessment of information needs with a small sample size may be unreliable. As suggested
by Butow and others, a periodic assessment of an individual patient’s information needs is
imperative, given the changing dynamics of patient’s information needs priorities.23, 39, 40
153
Because of the ability of the Thurstone scaling method to circumvent the ceiling
effect commonly seen in Likert-type summated scale, the utility of INQ in prioritizing the
information needs of large group of patient subgroups is promising. In this study, the
Thurstone scaling successfully produced a profile of information needs priorities in older
adults diagnosed with myeloma. The Thurstone’s paired comparison scaling has strong
psychometric properties as evidenced by statistically significant R-square statistics and
Kendall’s coefficient of agreement. These results validate the psychometric properties of
INQ instrument, as reported in other studies.15-20, 38, 41, 42
Several study limitations must be acknowledged. The generalizability of study
findings is limited to Caucasian men and women who are relatively highly educated and are
receiving care at a university-based comprehensive cancer center. The low response rate
(17.7%) is a potential source of response bias. One could argue that patients who did not
respond to the mailed flyer may be sicker than those who positively responded, and
potentially have different profiles of information needs. Additionally, the sample size was
small, which prevents the findings from being generalized to larger groups and also limits the
comparison of differences of information needs by other variables. Moreover, since this is a
cross-sectional study, the findings may not be applicable to myeloma patients who are
beyond the first 6 months from diagnosis and these results do not explain any aspect of
changes in information priorities over time. Despite these limitations, the study is still of
value because it’s the first one to identify information needs in myeloma patients and offers
additional hypotheses that need to be explored in older cancer patients. A longitudinal
approach in a future study should be able to document any changes in the priorities of
154
information needs at different periods of the disease trajectory (i.e., newly diagnosed, first
relapse, relapsed/refractory stages).
Implications for Practice and Research
The findings of this study have implications for nurses who are caring for older adult
patients newly diagnosed with myeloma. The relative priority of information needs presented
in this study could serve as a guide to elicit the individual information needs of future
myeloma patients. For example, Luker and colleagues have suggested that a profile of patient
information needs can be used as a clinical reference tool to create a structured approach to
information giving.22 By profiling information needs in advance, nurses may better
communicate myeloma-related information to their patients, thereby improving practice and
outcomes. A computerized version of INQ has already been tested and can efficiently
produce a patient’s priority of information needs immediately.41, 43 The use of this
technology, coupled with adequate knowledge and communication skills of the nurse, could
potentially offer some innovative solutions to the persistent problem of shrinking time for
patient education. Studies utilizing the computerized version of INQ to assess information
priorities in older adult populations are still needed.
The priorities of information needs elicited from this study should serve as a guide in
prioritizing information exchange with patient. In terms of treatment and prognosis, the
physician will be the best clinician to discuss the probability of survival in quantitative terms
to help patients make an informed decision. The physician has the full knowledge of
potential risks and benefits of therapy and should address this issue first with the patient.
Nurses can provide important supplemental information to patients in terms of how the
therapy will work and what side effects to expect. Self-care management strategies of side
155
effects can also be provided by the nurse. Of all the patient’s caregivers, the nurse is often in
contact with patient to provide additional information related to possible treatments,
prognosis, and self-care. Where information is scant, a nurse can always consult with the
multidisciplinary team members such as the clinical psychologist and social worker in order
to provide the information that the patient needs. Additionally, the nurse has the ability to
share important patient information with physicians, psychologists, and social workers
including information that relate to self-care, family, and social life. Although the profile of
information needs provides a quick summary of the types of information a patient needs, it is
still critical to perform an individual assessment of information needs for each patient. The
hierarchies of information needs identified in this study are based on grouped data that are
meant to provide general guides for the provision of information in clinical practice. As most
researchers suggest, individualized assessment of information needs at various time points is
imperative, since a patient’s information needs will usually shift over time. Longitudinal
studies are needed to help clinicians identify the appropriate frequency of assessment of
patient information needs.
Conclusion
The study findings demonstrate that older adults newly diagnosed with myeloma are
able to prioritize information needs. Oncology nurses and other health care professionals
responsible for providing patient education should assess patient’s information needs, and
could use the top three priorities relating to treatment, prognosis, and self-care reported in
this study as a starting board to elicit the patient’s information needs and share those
information needs with other members of the health care team.
156
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advanced cancer: a systematic review. Soc Sci Med. 2005;61(10):2252-2264.
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breast, prostate, and colorectal cancer patients: Results from a population-based
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Degner LF, Davison BJ, Sloan JA, et al. Development of a scale to measure
information needs in cancer care. J Nurs Meas. 1998;6(2):137-153.
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Husson O, Mols F, van de Poll-Franse LV. The relation between information
provision and health-related quality of life, anxiety and depression among cancer
survivors: a systematic review. Ann Oncol. 2010. Epub ahead of print.
6.
Walsh MC, Trentham-Dietz A, Schroepfer TA, et al. Cancer information sources
used by patients to inform and influence treatment decisions. J Health Commun.
2010;15(4):445-463.
7.
Mistry A, Wilson S, Priestman T, et al. How do the information needs of cancer
patients differ at different stages of the cancer journey? A cross-sectional survey.
JRSM Short Rep. 2010;1(4):30.
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Lewis N, Gray SW, Freres DR, et al. Examining cross-source engagement with
cancer-related information and its impact on doctor-patient relations. Health
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Ousley AL, Swarz JA, Milliken EL, et al. Cancer education and effective
dissemination: information access is not enough. J Cancer Educ. 2010;25(2):196-205.
10.
Tariman JD, Faiman B. Multiple Myeloma. In: Yarbro CH, Wujcik D, Gobel BH,
eds. Cancer Nursing Principles and Practice. 7th ed. Sadbury, MA: Jones and
Bartlett Publishers; 2011:1513-1545.
11.
Rome S. Patient teaching. In: Tariman JD, ed. Multiple myeloma: a textbook for
nurses. Pittsburgh, PA: Oncology Nursing Society; 2010:195-229.
12.
Palumbo A, Gay F, Falco P, et al. Bortezomib as induction before autologous
transplantation, followed by lenalidomide as consolidation-maintenance in untreated
multiple myeloma patients. J Clin Oncol. 2010;28(5):800-807.
13.
Minnema MC, van der Spek E, van de Donk NW, et al. New developments in the
treatment of patients with multiple myeloma. Neth J Med. 2010;68(1):24-32.
14.
Chouliara Z, Kearney N, Stott D, et al. Perceptions of older people with cancer of
information, decision making and treatment: a systematic review of selected
literature. Ann Oncol. 2004;15(11):1596-1602.
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Degner LF, Kristjanson LJ, Bowman D, et al. Information needs and decisional
preferences in women with breast cancer. JAMA. 1997;277(18):1485-1492.
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Wallberg B, Michelson H, Nystedt M, et al. Information needs and preferences for
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Davison BJ, Gleave ME, Goldenberg SL, et al. Assessing information and decision
preferences of men with prostate cancer and their partners. Cancer Nurs.
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screening for prostate cancer. Patient Educ Couns. 1999;37(3):255-263.
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diagnosed with breast cancer. J Adv Nurs. 1995;22(1):134-141.
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women newly diagnosed with breast cancer in Malaysia and the United kingdom.
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161
Appendix A
The Control Preferences Cards
162
163
1
CURRICULUM VITAE
Updated March 6, 2011
JOSEPH D. TARIMAN, PhD, APRN, BC
EDUCATION
Ph.D. in Nursing Science
2011
University of Washington
Seattle, WA
Post-MSN NP Certificate
2001
University of Miami
Coral Gables, FL
Master of Arts in Nursing
1993
Cebu Doctors’ University
Philippines
Bachelor of Science in Nursing 1991
University of the Visayas
Philippines
PROFESSIONAL LICENSES AND CERTIFICATIONS
Current Licenses and Certifications:
Registered Nurse
IL Dept. of Health RN041324045, expiration 05/31/2012
Advanced Registered Nurse Practitioner
IL Dept. of Health APN209004124, expiration 05/31/2012
IL Controlled Substance License-APN, Active, expiration 5/31/2012
Adult Health Nurse Practitioner
American Nurses Credentialing Center, #0359713-21 valid until 12/31/2011
Past Licenses and Certification:
Registered Nurse
2
WA Dept. of Health RN00163141, expiration 12/05/2010
Advanced Registered Nurse Practitioner
WA Dept. of Health AP30007274, expiration 12/05/2010
Oncology Certified Nurse®
Oncology Nursing Certification Corporation (2002-2010)
PROFESSIONAL EXPERIENCE: ACADEMIC
2009 – present
Predoctoral Fellow,
National Research Service Award
1F31NR011124-01A1
2006 – 2009
Predoctoral Research Fellow,
Biobehavioral Nursing Research
Program (NR007106-NIH/NINR)
University of Washington
Seattle, WA
1992- 1995
Clinical Instructor III
School of Nursing
Cebu Doctors’ University, Philippines
PROFESSIONAL EXPERIENCE: CLINICAL
University/Academic Medical Center
2010 – present
Advanced Practice Nurse
Northwestern University
Multiple Myeloma Program
Chicago, IL
2006 – 2009
Clinical Research Resident
ESRA-C Clinical Trial
University of Washington/Seattle
Cancer Care Alliance
2002 – 2006
Certified Nurse Practitioner
Northwestern University Medical
Faculty Foundation
Multiple Myeloma Program
Chicago, IL
3
2001- 2002
Nurse Manager
Hematology-Oncology/ Bone Marrow
Transplant Unit
University of Chicago Hospitals
Chicago, IL
2001 – 2004
Staff Nurse, Per Diem
Louis Weiss Memorial Hospital (UC of
Hospital Affiliates)
Medical-Surgical Oncology Unit
Chicago, IL
2000 – 2001
Staff Nurse III
University of Miami Hospitals and
Clinics-Sylvester Comprehensive
Canter Center
Hem-Oncology In-patient Unit
Miami, FL
Non-Academic Centers
2006 – 2009
Staff RN, Per Diem
Hematology-Oncology Unit
Evergreen Medical Center
Kirkland, WA
1999 – 2000
Assistant Director of Nursing
Star Multi Care Services, Inc.
Hollywood, FL
1998 – 1999
Hi-tech IV/Chemotherapy Case
Manager
Star Multi Care Services, Inc.
Hollywood, FL
1997- 1998
Director of Staff Development and
Training
Star Multi Care Services D/B/A
American Health Care Services
Miami Lakes, FL
4
1996 – 1997
Hi-Tech Field Supervisor
Home Health
Star Multi Care Services D/B/A
American Health Care Services
Miami Lakes, FL
1995 -1996
Hi-Tech Field RN
Home Health
Total Care Nursing Services
Miami, FL
PUBLICATIONS
* indicates data based work
# indicates work as contributing writer, Advance for NP
≈ indicates invited work
∞ indicates work as contributing editor, ONS Connect (Formerly ONS News)
Book
1. ≈Tariman, J. D. (2010). Multiple Myeloma: A Textbook for Nurses. Pittsburgh, PA:
Oncology Nursing Society Publishing Division.
Book chapters:
1. ≈Tariman, J. D. (2010). Chapter 1: Introduction. In. J. D. Tariman. Multiple
Myeloma: A Textbook for Nurses, Pittsburgh, PA: ONS Publishing Division. Pp. 1-7.
2. Tariman, J. D. (2005). Chapter 14: On the horizons: Future considérations. In. J. D.
Tariman. Multiple Myeloma: A Textbook for Nurses, Pittsburgh, PA: ONS Publishing
Division. Pp. 267-277.
3. Tariman, J. D. & Faiman, B (2011). Chapter 61: Multiple Myeloma. In. C.H.
Yarbro, M.H. Frogge, & M. Goodman. Cancer Nursing Principles and Practice, 7th
ed. Sudbury, MA: Jones and Bartlett Publishers. Pp. 1513-1545.
4. ≈ Tariman, J. D. (2005). Chapter 59: Multiple Myeloma. In. C.H. Yarbro, M.H.
Frogge, & M. Goodman. Cancer Nursing Principles and Practice, 6th ed. Sudbury,
MA: Jones and Bartlett Publishers. Pp. 1460-1489.
5
Publications – Peer Reviewed:
1. Tariman, J. D., Berry, D. L., Halpenny, B., Wolpin, S., Schepp, K., (2011).
Validation and testing of the Acceptability E-scale for web-based patient reported
outcomes in cancer care. Journal of Applied Nursing Research, 24(1), 53-58.
2. Tariman, J. D., Berry, D. L., Cochrane, B., Doorenbos, A., Schepp, K. (2010).
Preferred and actual level of participation during health care decision-making in
persons with cancer: A systematic review. Annals of Oncology, 21(6), 1145-1151.
3. Rehwaldt, M., Wickham., R, Purl, S., Tariman, J., Blendowski, C., Shott, S., et al.
(2009). Self-care strategies to cope with taste changes after chemotherapy. Oncology
Nursing Forum, 36(2), E47-56.
4. Colson, K., Doss, D.S., Swift, R., & Tariman, J. (2008). Expanding role of
bortezomib in multiple myeloma: Nursing implications. Cancer Nursing, 31(3), 239249.
5. Tariman, J. D., Love, G., McCullagh, E. J., Sandifer, S., IMF Leadership Board.
(2008). Peripheral neuropathy associated with novel therapies in patients with
multiple myeloma: Consensus statement of the IMF Nurse Leadership Board.
Clinical Journal of Oncology Nursing, 12(3, Suppl), 29-35.
6. Bertolotti, P., Bilotti, E., Colson, K., Curran, K., Doss, D., Faiman, B., the IMF
Nurse Leadership Board. (2008). Management of side effects of novel therapies for
multiple myeloma. Clinical Journal of Oncology Nursing, 12(3, Suppl), 9-12.
7. Faiman, B., Bilotti, E., Mangan, P.A., Rogers, K., & the IMF Nurse Leadership
Board. (2008). Steroid-associated side effects in patients with multiple myeloma:
Consensus statement of the IMF Nurse Leadership Board. Clinical Journal of
Oncology Nursing, 12(3, Suppl), 53-62.
8. Miceli, T., Colson, K., Gavino, M., Lilleby, K., & the IMF Nurse Leadership
Board. (2008). Myelosuppression associated with novel therapies in patients with
multiple myeloma: Consensus statement of the IMF Nurse Leadership Board.
Clinical Journal of Oncology Nursing, 12(3, Suppl), 13-19.
9. Rome, S., Doss, D.S., Miller, K., Westphal, J., & the IMF Nurse Leadership Board.
(2008). Thromboembolic events associated with novel therapies in patients with
multiple myeloma: Consensus statement of the IMF Nurse Leadership Board.
Clinical Journal of Oncology Nursing, 12(3, Suppl), 21-27.
10. Smith, L.C., Bertolotti, P., Curran, K., Jenkins, B., & the IMF Nurse Leadership
Board. (2008). Gastrointestinal side effects associated with novel therapies in
patients with multiple myeloma: Consensus statement of the IMF Nurse Leadership
Board. Clinical Journal of Oncology Nursing, 12(3, Suppl), 37-51.
11. ≈ Tariman, J. D. (2007). Lenalidomide: A promising novel agent for patients with
relapsed and/or refractory multiple myeloma. Clinical Journal of Oncology Nursing,
11, 569-574.
12. Rodriguez, A., Tariman, J. D., Enecio, T., Estrella, M. E. (2007). The role of high
dose chemotherapy supported by hematopoietic stem cell transplantation in patients
with MM: Implications for nursing. Clinical Journal of Oncology Nursing, 11, 579589.
6
13. ≈ Tariman, J. D. (2007). Current therapies for multiple myeloma. Journal of
Infusion Nursing, 30(2), 113-118.
14. Chanan-Khan, AA., Kaufman, JL, Mehta, J., Richardson, P.G., Miller, K.C., Lonial,
S., Munshi, N. C., Schlossman, R. Tariman, J., et al., (2007). Activity and safety of
bortezomib in multiple myeloma patients with advanced renal failure: A multicenter
retrospective study. Blood, 109(6), 2604-2606.
15. Tariman, J. D. & Estrella, E. (2005). The changing treatment paradigm in patients
with newly diagnosed multiple myeloma: Implications to nursing practice. Oncology
Nursing Forum, 31, E127-E138.
16. ≈Colson, K., Doss, D. S., Swift, R., Tariman, J., & Thomas, T. E. (2004).
Bortezomib, a newly approved proteasome inhibitor for the treatment of multiple
myeloma: Nursing implications. Clinical Journal of Oncology Nursing, 8, 473-480.
17. ≈ Tariman, J. D. (2004). Clinical applications of magnetic resonance imaging in
patients with multiple myeloma. Clinical Journal of Oncology Nursing, 8, 317-320.
18. Tariman, J. D. (2003). Understanding novel therapeutic agents for multiple
myeloma. Clinical Journal of Oncology Nursing, 7, 521-528.
19. Tariman, J. D. & Lemoine, C (2003). Bortezomib. Clinical Journal of Oncology
Nursing, 7, 687-689.
20. Tariman, J. D. (2003). Thalidomide: Current therapeutic uses and management of its
toxicities. Clinical Journal of Oncology Nursing, 7, 143-147.
Published Abstracts – Peer Reviewed:
1. Heuck, C., Mehta, J., Tariman, J., Pulliam, N., Yu, Y., Bhagat, T., et al. (2010).
Epigenomic profiling of multiple myeloma shows widespread stage specific
alterations in DNA methylation that occur early during myelomagenesis [Abstract
#784]. Available at: http://ash.confex.com/ash/2010/webprogram/Paper33425.html
2. Tariman, J. D., Berry, D. L., Cochrane, B., Doorenbos, A., & Schepp, K. (2010).
Treatment decision-making in older adults newly diagnosed with myeloma: A
Preliminary analysis. Communicating Nursing Research (CD-ROM). Poster
presentation at WIN Conference 2010.
3. Tariman, J. D. (2009). Preferred and actual level of participation during treatment
decisions in persons with cancer: A systematic review. Communicating Nursing
Research (CD-ROM). Poster presentation at WIN Conference 2009.
4. Tariman, J. D. (2009). A proposed theoretical model of decision making
participation, decisional conflict, regret, satisfaction, and QOL in adults with cancer.
Communicating Nursing Research (CD-ROM). Poster presentation at WIN
Conference 2009.
5. ≈Moran, D., Bertolotti, P., Bilotti, E., Colson, K., Curran, K., Doss, D., Faiman, B.,
Tariman, J. D., et al. (2009). Long-term care guidelines for myeloma patients
[Abstract # 30]. XII Int’l Myeloma Workshop- Washington, DC. February 27, 2009.
6. Tariman, J. D. (2008). Decision-making in oncology settings: A concept analysis.
Communicating Nursing Research, 41, 336.
7. Tariman, J.D., Berry, D.L., Halpenny, B., & Wolpin, S. (2008). Validation and
testing of the Acceptability E-scale. Communicating Nursing Research, 41, 372.
7
8. ≈Bertolotti, P., Bilotti, E., Colson, K., Curran, K., Doss, D., Faiman, B., Tariman, J.
D., et al. (2007). Nursing guidelines for enhanced patient care [Abstract # PO-1102].
Haematologica, 92(6Suppl 2), 211.
9. * Tariman, J. D., Paice, J., Mehta, J., Duffey, S. & Singhal, S. (2007). Quality of life
(QOL) in older adults undergoing autologous stem cell transplant (ASCT) [Abstract
No. 101]. Oncology Nursing Forum, 34(1), 207.
10. * Rehwaldt, M., Purl, S., Tariman, J. D., Blendowski, C., Lappe, M., Shott, S., &
Wickman, R. (2006). A study of taste change strategies [Abstract No. 256]. Oncology
Nursing Forum, 33(2), 467.
11. * Gidron, A., Tariman, J., Shabbir, M., Mehta, J., Singhal, S. (2005). High plasma
cell labeling index is an adverse prognostic factor in bortezomib-treated patients with
relapsed myeloma [Abstract No. 3471]. Blood, 106(11), 968A-969A.
12. * Kut, V., Mehta, J., Tariman, J., Olsson, A., Singhal, S. (2004).
Osteonecrosis of the jaw in myeloma patients receiving pamidronate or zoledronate
[Abstract No. 4933]. Blood, 104, 315b.
13. * Singhal, S., Tariman, J., Smith, K., Riley, M.B., Mistina, J., Mehta, J. (2003).
Bortezomib is effective therapy for relapsed multiple myeloma [Abstract No. 5289].
Blood, 102(11), 390B-391B.
14. * Singhal, S., Goolsby, C., Taylor, R., Tariman, J., Karpus, W., Mehta, J. (2003).
Identifying novel therapeutic targets in myeloma: CD20, CD25 and CD52 expression
on malignant plasma cells [Abstract No. 5221]. Blood, 102(11), 373B-373B.
Publications - Non-Peer Reviewed:
1. Tariman, J. D. (2010). Role specialization in oncology: Finding your niche. ONS
Connect, 25(5), 6-9.
2. Tariman, J. D. (2010). Where to draw the line: Professional boundaries in social
networking. ONS Connect, 25(2), 10-13.
3. # Tariman, J. D. (2009). Promoting testicular health: Strategies that work. Advance
for Nurse Practitioners, 17(11), 18.
4. Tariman, J. D. (2009). Do you know how to care for older adults? ONS Connect,
24(12), 8-11.
5. Tariman, J. D. (2009). Mentoring in publication: A lifelong legacy. ONS Connect,
24(1), 8-12.
6. Tariman, J. D. (2009). Men and Alzheimer’s disease: Sex differences in risk.
Advance for Nurse Practitioners, 17(2), 23.
7. # Lowe, J. & Tariman, J. D. (2008). Lower extremity amputation: Black men with
diabetes overburdened. Advance for Nurse Practitioners, 16(11), 28.
8. # Tariman, J. D. (2008). Suicide among boys and men: More challenges ahead.
Advance for Nurse Practitioners, 16(8), 25.
9. ∞Tariman, J. D. (2008). Technologic advancements in cancer care: Changing
practice, changing patients' lives. ONS Connect, 23(7), 8-11.
10. # Tariman, J. D. (2008). Life span vs. healthy life expectancy: Where did men go
wrong? Advance for Nurse Practitioners, 16(5), 26.
8
11. # Tariman, J. D. (2008). Obesity in men. Advance for Nurse Practitioners, 16(2), 26.
12. # Tariman, J. D. (2007). Subfertility in men. Advance for Nurse Practitioners,
15(11), 25.
13. # Tariman, J. D. (2007). Osteoporosis in men: A real threat. Advance for Nurse
Practitioners, 15(8), 21.
14. # Tariman, J. D. (2007). Testosterone replacement. Advance for Nurse Practitioners,
15(2), 22.
15. ∞ Tariman, J. D. (2007). Understand substance abuse in nurses. ONS Connect,
22(8), 18.
16. ∞ Tariman, J. D. (2007). Straight talk. When should you blow the whistle for ethical
reasons? ONS Connect, 22(2), 22-23.
17. ∞ Tariman, J. D. (2007). Exploring an unfamiliar world: Advice for novices delving
into the research arena. ONS Connect, 22(12), 12-16.
18. ∞ Tariman, J. D. (2007). Emergency preparedness: What have we learned? ONS
Connect, 22(9), 8-12.
19. # Tariman, J. D. (2007). Diabetes in minority men. Advance for Nurse Practitioners,
15(2), 18.
20. ∞ Tariman, J. D. (2006). Is your chapter on the road to success or experiencing
some bumps along the way? ONS News, 21(12), 1, 4-5.
21. ∞ Tariman, J. D. (2006). ONS members bring oncology nursing-sensitive patient
outcomes to the bedside. ONS News, 21(10), 1, 4-6.
22. # Tariman, J. D. (2006). Prostate cancer: Screening and early detection. Advance for
Nurse Practitioners, 14(9), 20.
23. # Tariman, J. D. (2006). Smoking cessation in men. Advance for Nurse
Practitioners, 14(6), 21.
24. # Tariman, J. D. (2006). Substance abuse among adolescent boys. Advance for
Nurse Practitioners, 14(3), 18.
25. Berenson, J., Tariman, J. D., Bensinger, W., Doss, D., & Swift, R. A. (2004). ONS
News: Spotlight on Symposia,19(9), 79-80.
26. Tariman, J. D. (2003). Performance improvement for myelosuppressed patients:
Institutional case studies. In. D. Wujcik & C. Lemoine. Performance improvement in
myelosuppression management: Continuing the ATAQ initiatives. Pittsburgh, PA:
Oncology Nursing Society. P33-37.
27. Tariman, J. & Hammer, M. (2009). The PSONS research survey results. Puget Sound
Oncology Nursing Society Quarterly, 32(2), 11, 13-14.
28. ≈ Tariman, J. D. (2008). Multiple myeloma: Maximizing the therapeutic benefits of every
novel agent. Puget Sound Oncology Nursing Society Quarterly, 31(3), 1, 3-5.
29. ≈ Tariman, J. D. (2003). Clinical Update: Thalidomide in patients with multiple myeloma.
Chicago Chapter Oncology Nursing Society Newsletter, Lead article, Fall 2003.
30. ≈ Tariman, J. D. (2003). IMiDs: Novel thalidomide analogue for multiple myeloma.
Chicago Chapter Oncology Nursing Society Newsletter, Lead article, Summer 2003
RESEARCH
Pre-Doctoral Grant
9
1. Pre-Doctoral Fellow, Ruth L. Kirschstein National Service Award funded by the National
Institute of Health, National Institute of Nursing Research, Individual Fellowship Grant
#1F31NR011124-01A1
Project title: Treatment decision making in older adults newly diagnosed with multiple
myeloma; September 2009 – March 2011 (18 months full support).
Sponsors: Barbara Cochrane, PhD, RN, FAAN (University of Washington) & Donna L.
Berry, PhD, RN, FAAN (University of Washington and Dana Farber Cancer Institute).
2. Pre-Doctoral Fellow, Ruth L. Kirschstein National Service Award funded by the National
Institute of Health, National Institute of Nursing Research, Institutional Grant #T32-;
Training in Biobehavioral Nursing Research; September 2006 – September 2008 (full
support).
Mentor: Donna L. Berry, PhD, RN, FAAN
Research residency: Electronic Self Report Assessment for Cancer (ESRA-C)
Participated in the recruitment of participants and coded subjective data. Performed
validation and testing of the Acceptability E-scale. Published manuscript related to
psychometric properties of Acceptability E-scale.
Graduate Thesis
Doctoral dissertation: Treatment decision making in patients with newly diagnosed multiple
myeloma – Fall 2008 to Winter 2011
Master’s thesis: Incidence of Nosocomial Infections in OR and ICU at Cebu Doctors
Hospital: Implications for Infection Control Program. Principal Investigator - January 1992
to June 1993.
Collaborative Research
“QOL post-ASCT among elderly adults with multiple myeloma” Chicago Oncology Nursing
Society Research Grant, October 2002 to October 2005, ($1,500 Co-Principal Investigator
with Judith Paice, PhD, RN)
Pharmaceutical and Cooperative Sponsored Research
10
Sub-Investigator Role:
A. Assessment and monitoring of side effects and serious adverse events.
B. Authorized designee to sign case report forms and documentation of response to
investigational therapy (Form 1572).
1. CC-5013-MM-016 Lenalidomide Expanded Access Program for Patients with
Relapsed and/or refractory MM. November 2005-March 2006
2. ECOG4A03 A Randomized Phase III Study of CC-5013 plus Dexamethasone versus
CC-5013 plus Low Dose Dexamethasone in Multiple Myeloma with Thalidomide
plus Dexamethasone Salvage Therapy for Non-Responders – May 2005 to March
2006
3. An Open-Label Phase II Trial of Motexafin Gadolinium (MGD) in Patients with
Relapsed or Refractory Multiple Myeloma. Sub-Investigator – November 2004 to
March 2006.
4. CC-5013-MM-014: A Multicenter, Single-Arm, Open-Label Study to Evaluate the
Safety and Efficacy of Single-Agent CC-5013 in Subjects with relapsed and
Refractory Multiple Myeloma. Sub-Investigator – July 2003 to March 2006.
5. CC-5013-MM-009: A Multicenter, Randomized, Parallel-Group, Double-Blind,
Placebo-Controlled Study of CC-5013 Plus Dexamethasone in Previously Treated
Subjects with Multiple Myeloma. Sub-Investigator – May 2003 to June 2004.
6. E1A00: A Randomized Phase III trial of Thalidomide Plus Dexamethasone Versus
Dexamethasone in Newly Diagnosed Multiple Myeloma. Sub-Investigator – January
2003 to May 2004.
7. M34102-052: An Open-Label, Randomized Study to Assess Two different Strategies
of Combining Dexamethasone with Velcade in Patients with Relapsed Multiple
Myeloma who Have Failed Four or More Lines of Therapy. Sub-Investigator –
January 2003 to May 2003.
8. M34101-040: An International, Non-Comparative, Open Label Study of PS-341
Administered to Patients with Multiple Myeloma Who Experienced Relapsed or
Progressive Disease After Receiving At Least Four Previous Treatment Regimens or
Experienced Progressive Disease After Receiving Dexamethasone in Millennium
Protocol M34101-039. Sub-Investigator – February 2002 to May 2003
9. An International, Multi-Center, Randomized, Open Label Study of PS-341 Versus
High-Dose Dexamethasone in Patients with Relapsed or Refractory Multiple
Myeloma Protocol M34101-039. Sub-Investigator – February 2002 to May 2003
10. A phase II, Open Label, Extension Study to Provide PS-341 to Patients Who
Previously Participated in a PS-341 Clinical Study and Who May Benefit from ReTreatment with or Continuation of PS-341 Therapy Protocol M34101-029. SubInvestigator – February 2002 to April 2003
11. A phase II, Open Label Study of PS-341 in Patients with Relapsed/Refractory
Myeloma Protocol M34101-025. Sub-Investigator – February 2002 to June 2002
12. Randomized Phase III Study of Dexamethasone With or Without Genasense (Bcl-2
Antisense Oligonucleotide) in Patients with Relapsed or Refractory Multiple
Myeloma Protocol GMY302. Sub-Investigator – February 2002 to May 2003.
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PROFESSIONAL CONSULTATIONS
2006 – present
International Myeloma Foundation
North Hollywood, CA
Chair – Peripheral Neuropathy
Group, Nurse Leadership Board
2005 – present
Men’s Health Network
Washington, D.C.
Member, Board of Advisors
2003 – present
Millennium Pharmaceuticals
Cambridge, MA
Clinical Consultant
Member, Speaker’s Bureau
2007 – present
Kyphon, Incorporated
Clinical Consultant
Member, Speaker’s Bureau
PROFESSIONAL PRESENTATIONS
International
International Society of Nurses in Cancer Care (ISNCC) Biennial Conference. Computerized
Assessment for Patients with Cancer: aka Electronic Self Report Assessment-Cancer (ESRAC). Singapore City, Singapore. August 2008.
XI IMW - Nursing guidelines for enhanced patient care. Poster Session, XI International
Myeloma Workshop. Kos, Greece. June 2007.
9th International Myeloma Workshop-Cleveland Clinic Nursing Symposium – Management
of Chemotherapeutic Side Effects, Sydney, Australia April 2005
National Conference Lectures
1. ONS Institute of Learning – Survivorship Care Issues in Patients with Myeloma.
November 13, 2010.
2. ONS Congress – Changing Patient Care in Multiple Myeloma: The IMF Nurse
Leadership Board’s Long-Term Survivorship Care Plan. May 13, 2010.
3. ONS Congress – Leading the Way: Managing Multiple Myeloma for the Long-term.
Presenter – Novel therapies for multiple myeloma. San Antonio, TX, May 1, 2009.
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4. ONS Congress – Navigating the clinical course of multiple myeloma patient care.
Presenter – Induction therapy for patients ineligible for stem cell transplant. San
Antonio, TX, April 30, 2009.
5. ONS Congress – Oncology Nursing Patterns in Multiple Myeloma. Presenter –
Management of Treatment-related Effects. San Antonio, TX. April 29, 2009.
6. ONS Congress- Leading the Way: Managing Multiple Myeloma for the Long-Term.
Presenter – Update on Novel Therapies. San Antonio, TX. May 1, 2009.
7. ONS Congress – Navigating the Clinical Course of Multiple Myeloma Patient Care.
Presenter – Induction Therapies for Transplant Ineligible Patients. San Antonio, TX.
April 30, 2009.
8. ONS Congress – Changing Paradigms in the Treatment of Multiple Myeloma: A
Nurse-Centric Case-Based Discussion– Chair and Presenter, Symposium, Oncology
Nursing Society Annual Congress. Philadelphia, PA. May 17, 2008
9. ONS Congress – Staying Upright: Managing Spinal Complications for Patients with
Cancer – Symposium, Oncology Nursing Society Annual Congress. Philadelphia, PA.
May 17, 2008
10. ONS Congress – The IMF Nurse Leadership Board Consensus of Care: New Insights
on Novel Therapies in Multiple Myeloma – Symposium, Oncology Nursing Society
Annual Congress. Philadelphia, PA. May 16, 2008
11. ONS Congress – The Spectrum of Multiple Myeloma Patient Care: A Case-Based
Assessment for Oncology Nurses – Symposium, Oncology Nursing Society Annual
Congress. Philadelphia, PA. May 18, 2008
12. ONS Congress - Management of Novel Therapeutics’ Side Effects – A Nurse Centric
Model. Symposium, Oncology Nursing Society Annual Congress. Las Vegas, NV.
April 2007
13. WIN Conference – Decision Making in Oncology Settings and Validation and
Testing of the Acceptability E-scale. Poster Session, Western Institute of Nursing
Annual Conference. Garden Grove, CA. April 2008.
14. ONS Congress – Updates on metastatic spine disorders. Symposium. ONS Annual
Congress. Las Vegas, NV. April 2007.
15. NCNRC - Quality of life (QOL) in older adults undergoing autologous stem cell
transplant (ASCT). Poster Session, National Cancer Nursing Research Conference.
Hollywood, CA. February 2007
16. ONS Annual Congress – Frontline Therapies for Multiple Myeloma, Orlando, FL
April 2005
17. ONS Annual Congress - Multiple Myeloma: A New Treatment Paradigm, Anaheim,
CA April 2004
18. ONS Fall Institute – Proteasome Inhibition in Multiple Myeloma, Philadelphia, PA
November 2003
19. ONS Annual Congress - Bortezomib for Relapsed and Refractory Multiple Myeloma
– Denver, CO April 2003
Regional Conference Lectures
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1. Annual Scripps Advanced Practice Nurses Oncology Conference. Presenter – Plasma
cell Disorders. San Diego, CA. February, 2009.
2. Annual NW Oncology Conference - Advances in Multiple Myeloma. Annual
“Concepts in Oncology Conference". Symposium, Boise, ID. October 2007
3. Annual Scripps Cancer Center Oncology Conference – Novel Therapies for Patients
with Multiple Myeloma and Nursing Management of Common Toxicities, San Diego,
CA September 2007
4. Myeloma: Disease overview, treatment considerations and nursing implications.
Orlando, FL. January 2006
5. Myeloma: Disease overview, treatment and clinical update on bortezomib therapy.
Milwaukee, WI, December 2005
6. International Myeloma Foundation – Patient and Family Seminar – QOL Session,
Northbrook, IL June 2005
7. Scripps’s Cancer Center Annual Advanced Practice Nurse Conference – Advances in
Multiple Myeloma, San Diego, CA March 2005
8. Millennium Pharmaceutical Nurse/Pharmacist Consultant’s Meeting – Clinical
Update on Bortezomib in Hematologic Malignancies, Beverly Hills, CA November
2004
9. Cornell-NYU/Letters& Science Annual Oncology Conference – Advances in
Hematologic Malignancies, New York City, NY October 2004
10. Northwestern Memorial Hospital’s Annual Oncology Nursing Conference – Program
Chair and Presenter: Novel Agents for Multiple Myeloma, Chicago, IL October 2004
11. Annual Oncology Care Conference-“Novel Therapeutic Options for Myeloma”,
Honolulu, HI July 2004
12. Chicago Myeloma Foundation - Symptom Management in Patients with Multiple
Myeloma, Chicago, IL September 2003
Online Educational Programs
1. Christiansen, N., Bensinger, W., Colson, K., Harvey, R. D., Lonial, S., & Tariman,
J. D. (2009). Module 8: Building the best myeloma regimens: Is there a blueprint?
Available at http://www.questmeded.com/cebin/owa/pkg_disclaimer_html.display?ip_mode=secure&ip_company_code=P4CC&i
p_test_id=14504&ip_cookie=32248899
2. Tariman, J. D. & Harvey, R. D. (2009). Module 2: Clinical Considerations and
Implications for Optimizing Patient Outcomes in Multiple Myeloma. Available at
http://www.clinicaloptions.com/Oncology/Management%20Series/Multiple%20Myel
oma.aspx
3. Harvey, R. D & Tariman, J. D. (2009). Module 1: Mechanisms of Action and
Clinical Advances in the Treatment of Multiple Myeloma. Available at
http://www.clinicaloptions.com/Oncology/Management%20Series/Multiple%20Myel
oma.aspx
4. Tariman, J. D., Harvey, R. D. & Colson, K. (2009). Module 6: Multiple Myeloma:
Relapsed/Refractory Disease – Care Team Considerations. Available at
http://www.questmeded.com/ce-
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5.
6.
7.
8.
bin/owa/pkg_disclaimer_html.display?ip_cookie=31242293&ip_mode=secure&ip_te
st_id=14409&ip_company_code=P4CC
Tariman, J. D. & Harvey, R. D. (2009). Module 4: Multiple Myeloma Induction and
Transplant Strategies – Care Team Considerations. Available at
http://www.questmeded.com/cebin/owa/pkg_disclaimer_html.display?ip_cookie=31242245&ip_mode=secure&ip_te
st_id=13569&ip_company_code=P4CC
Tariman, J. D., Fonseca, R., & Colson, K. (2008). Changing Paradigms in the
treatment of Multiple Myeloma: Nurse-Centric Case-Based Discussions. Available at
http://old.imeronline.com/315_mm/home.html
Lonial, S. & Tariman J. D. (2008). Clinical Updates in the Management of Patients
With Multiple Myeloma Case-Based DVD. Available at
http://www.thecbce.com/onlineEdu_detail.aspx?act_id=258
Smith, L., Tariman, J. D., & Harvey, R. D. (2008). Module 2: Diagnosis and Staging
of Multiple Myeloma (MM) and Front-Line Therapy for the Transplant-Ineligible
Patient: Care Team Considerations. Available at http://www.questmeded.com/cebin/owa/pkg_disclaimer_html.display?ip_cookie=31242277&ip_mode=secure&ip_te
st_id=13567&ip_company_code=P4CC
Institutional Lectures
1. Harborview Medical Center – Multiple Myeloma: Maximizing the Therapeutic
Benefits of Every Novel Agent. Seattle, WA. December 09, 2008.
2. FHCRC – Symptom Management and Nurse’s Role in Patient Care. International
Myeloma Foundation Patient and Family Seminar. Seattle, WA. January 2008.
3. FHCRC- Management of Renal Insufficiency in Patients with Multiple Myeloma.
Fred Hutchinson Cancer Research Center Nursing Education Day. Seattle, WA.
January 2007.
4. NW Oncology Cancer Center - Vertebroplasty and kyphoplasty in patients with
metastatic bone disease. Olympia, WA. February 7, 2007.
5. Fort Collins-ONS Chapter – “Multiple Myeloma, Disease Overview and Advances in
Therapy”, Fort Collins, CO. June 28, 2006.
6. Lutheran General Hospital- “Nursing Management of Patients with Multiple
Myeloma”, Park Ridge, IL. March 3, 2006.
7. Kaiser-Permanente Oncology – “Nursing Management of Patients with Multiple
Myeloma”, Portland, OR. January 19, 2006.
8. Palm Beach Myeloma Support Group – “Advances in Myeloma Therapy”, Palm
Beach, FL May 6, 2005
9. Mid Chesapeake Bay ONS- “Novel therapies for Patients with Myeloma”,
Washington, D.C. November, 2004
10. UPMC Cancer Center- “Proteasome Inhibition-A Novel Option for the Treatment of
Myeloma”, Pittsburgh, PA, June 10, 2004
11. Chicago ONS Chapter - Multiple Myeloma Update, Chicago, IL March 2004
12. Waukesha Memorial Hospital - Multiple Myeloma: Overview and Novel Therapeutic
Agents, Waukesha, WI May 2003
15
HONORS AND AWARDS
2008
Best Student Poster Award
Western Institute of Nursing
Conference, Garden Grove, CA 2008
2007
Best Poster Award
National Cancer Nursing Research
Conference, Hollywood, CA 2007
2006
Achievement Rewards for College
Scientists
Seattle, WA
2006
Betty Irene and Gordon Moore
Fellowship, UCSF
San Francisco, CA
2005
Outstanding New Member Award
Oncology Nursing Society (ONS)
Chicago Chapter
Chicago, IL
1991
Academic Excellence Award
13th Place, Philippines Board of Nursing
Examination
Manila, Philippines
1991
Cum Laude
BSN Class 1991
Cebu, Philippines
PROFESSIONAL SERVICES
2008 – present
Editorial board member – The Oncology
Nurse
2006 – 2010
Contributing writer – Advance for Nurse
Practitioners-Men’s Health Column
16
2005 – 2010
Contributing editor - ONS News
2005 – 2009
Continuing Education Pilot Reviewer –
ONS ECCIT
2003 – present
Review board member - Clinical
Journal of Oncology Nursing
2004 – present
Review board member - Oncology
Nursing Forum
2004- 2006
Chicago Oncology Nursing Society –
Board of Director and Research
Committee Member
2002 – 2006
Reviewer - Northwestern University
Clinical Protocol and Scientific Review
Monitoring Committee
PROFESSIONAL ORGANIZATIONS
Council for the Advancement of Nursing Science
Member
2009-present
Sigma Theta Tau Honors Society
Member
2008 – present
Western Institute of Nursing
Member
2007-present
Oncology Nursing Society
Member and Contributing Editor for
1998- present
ONS Connect (2005-2010)
17
Puget Sound Chapter Oncology Nursing Society
Chair, Research Committee
2006 - 2010
Chicago Chapter Oncology Nursing Society
Director-at-large
2001-2006
PROFESSIONAL TEACHING:
1992-1995
Specialization: Adult Medical-Surgical
Nursing – Three years of classroom and
clinical instruction, BSN program
Cebu Doctors’ University
Philippines
Invitational Speech
March 2006
Commencement Speaker
University of the Visayas
College of Nursing
Cebu, Philippines
Community or Public Services Activities:
1. MM Fighters Seattle Patient Support Group, Guest speaker. Topic: Treatment
Decision Making and Quality of Life. January 2008.
2. Chicago Gilda’s Club – Support Group Facilitator, January 2006.
3. Men’s Health Network – Board of Directors, Member – July 2005 to present
4. Multiple Myeloma Research Foundation – Volunteer, fundraising activities. Provides
free lectures to patients and family members – March 2002 to March 2006.
5. International Myeloma Foundation – Volunteer, fundraising activities. Provides free
lectures to patients and family members March 2002 –March 2006.
6. Medical Mission to Haiti – Health care volunteer, provided basic health care services
to the underserved children and families in Port-au-Prince, Haiti – July 2000.