2013 Special Topics Conference
Peaks and Pitfalls in Longitudinal
Analysis of Symptom Outcome Data
Terri S. Armstrong, PhD, ANP-BC, FAANP
University of Texas health Science Center at Houston
Special Thanks:
AAN: Planning Committee, Laura Smothers
UTHSC-SON: Nancy Bergstrom, Dean Patricia
Starck
COI: Research Support
Schering Plough
Merck
Genentech
RESEARCH SUPPORT:
RTOG 0525/0825: NCI U10CA 21661,
U10CA37422, ABC2,
MERCK PHARMACEUTICALS, GENENTECH
FATIGUE PILOT STUDY: CERN RESEARCH
FOUNDATION
INNOVATIONS IN LONGITUDINAL DATA
COLLECTION AND ANALYSIS
Success consists of
going from failure to
failure without loss
of enthusiasm
-Winston Churchill
SYMPTOMS EXPERIENCE MODEL
Armstrong, T.S. (2003), ONF; Armstrong et al, (2004) Journal of Nursing Scholarship
The Science of Symptom Management
I
Exposure
I
Biologic
Trigger or
Process
Genetic
Susceptibility
Symptom or
Toxicity
I
I
10
9
8
7
6
5
4
3
2
1
0
60
54
48
42
36
30
24
18
12
6
0
Pain
Fatigue
Nausea
Sleep
Distress
Shortness
of Breath
Lack
of Appetite
Sadness
Attention
WBC
-14
-7
0
7
14
Treatment
Target
Intervention
21
28
35
WBC (x10^9/L)
Symptom Score
Targeted Intervention
42
Biologic
Correlate
The Science Behind Symptom
Management: INTERLOCKING IDEAS
•
Recognize
importance
and Accurately
Measure
Predict whose
at Risk
(Clinical and
Genomic)
Develop Biologically
Based & Practical
Interventions
PROs Used in the Analysis
Symptom Burden: MDASI-BT
Overall
Scores
•
•
-
Global Symptom Burden
Mean of all 22 symptoms
Interference (6 items)
Activity Related
Interference
- Mood Related Interference:
6 multi-item Groupings
Factor
1.Affective Factor
Grouping 2.Cognitive Factor
3. Neurologic Factor
4. Treatment-related Factor
5. Generalized Disease Factor
6. GI Factor
Longitudinal Data
Causality
Course
before the
index
episode
Prodrome
Index
episode
Course
between the
index
episode and
follow-up
Follow-up or
Outcome
Assessment
Causality
Adamis, 2009
Advantages/Disadvantages of
Longitudinal Data
• ADVANTAGES:
– Often provide more informative
data
– They allow the study of
individual dynamics
(such as age and cohort effects)
– They allow assessment of the
time order of events
• DISADVANTAGES:
– Attrition and missingness
– Need for special statistical
analysis
(individual versus time)
‘It isn’t the
mountain ahead
that wears you
out;
It’s the grain of
sand in your shoe’
Diggle, Heagerty, Liange, & Zeger, 2002;
Meard, 1991; & Adamis, 2009
Brain Tumor Background
• Tumors that arise from the constituent
elements of the CNS & primarily stay within
the CNS
• An estimated 51,410 new cases of primary
nonmalignant and malignant brain tumors
estimated for 2012 (21,810
malignant)1
• Above represents 1.35% of
cancers1
• An estimated 12,760 deaths will be
attributed to primary malignant brain tumors
in the U.S. in 20051; this represents 2.4% of
all cancer deaths2
1. CBTRUS: Statistical Report on Primary Brain Tumors in the United States,.
www.cbtrus.org/factsheet.htm
2. SEER.cancer.gov/CSR
Rationale for Program of Research
 Patients with CNS tumors often suffer devastating effects as a consequence of the
tumor and/or treatment
 Often unable to return to work from the time of diagnosis and studies report
patients spend the majority of their lives feeling ill and unable to perform usual
activities (Fobair et al, 1990; Salander et al, 2000; Strang & Strang, 2001)
 Limitations of current outcomes assessment
 CNS tumor treatments are often similar in efficacy and survival (Stupp et al, 2005)
 Current imaging is limited by technique, interpretation, and changing impact of
targeted agents and ‘The Avastin Effect’ (Chamberlain et al, 2006; Norden et al,
2008); and pseudoprogression (Chamberlain et al, 2007)
 Tumor related Symptoms and Toxicity associate with therapy has been widely
reported, but not collected in a systematic or rigorous way. (Armstrong et al, 2005;
Scheibel, et al, 1996; Correa et al, 2007)
 Traditional endpoints do not necessarily reflect clinical benefit
Standard Treatment
Surgery
It’s like deja-vu, all
over again
-Yogi Berra
Concurrent
chemoradiation
Adjuvant
chemotherapy
(6 weeks)
(12 months)
Net Clinical Benefit Analysis
Comparative Impact of
Of Radiation Therapy Oncology
Treatment on Patient Reported
Group 0525: A phase III Trial
Outcomes (PROs) in Patients
comparing conventional
with Glioblastoma (GBM)
Adjuvant Temozolomide with
Enrolled in RTOG 0825
Dose-Intensive Temozolomide
In Patinets with newly iagnosed
GBM
Jeffrey S. Wefel, PhD , Meihua
Wang, PhD Minhee Won, MA,
Andrew Bottomley, PhD, Tito R.
Mendoza, PhD, Corneel Coens,
MSc, Maria Werner-Wasik, MD,
David G. Brachman, MD, Ali K.
Choucair, MD, Mark R. Gilbert,
MD, Minesh Mehta, MD
Won, M., Wefel, J.S.,
Gilbert, M.R., Pugh, S.L.,
Wendland, M., Brachman,
D., Komaki, R., Crocker I.
, J., Robins, H.I. ., Lee, R.,
& Mehta, M.
Biologic Correlates of
Fatigue in GBM Patients
Undergoing Radiation
Therapy: A Pilot Study
Alvina Acquaye, MS, David
Balachandran, MD, Elizabeth VeraBolanos, MS, Mark R. Gilbert, MD,
Duck-Hee Kang, PhD, Anita
Mahajan, MD
Top 5 List
•
•
•
•
•
Study Planning
Study Design
Conduct of the Study
Data Analysis
Data Reporting
'The only thing you'll
find on the summit of
Mount Everest is a
divine view. The things
that really matter lie
far below.’
-Roland Smith
Study Planning
‘I AM THANKFUL TO ALL THOSE
WHO SAID NO- IT IS BECAUSE OF
THEM I DID IT MYSELF’
-ALBERT EINSTEIN
Steps in Planning Use of Pros in Longitudinal Studies
Identify the relevant domains to measure:
What are the areas that the particular therapy are known or hypothesized to impact?
Development of a conceptual framework:
Outline the proposed relationships among the disease, treatment and PRO domains.
Identify candidate approaches to measuring the domains:
Is there an existing instrument that is psychometrically validated and feasible for use?
Synthesize the information to design the final measurement strategy:
Develop hypotheses and measureable outcomes based on the identified relationships
between primary outcome and PRO domains. Identify timepoints that are important to
capture, considering feasibility and completion of data.
Conducting the Study: The PITFALLS
What I have learned
And Yogi Says:
• Seek input from others
‘IF YOU DON’T KNOW WHERE YOU
• Be active in the data
collection
YOU CAN OBSERVE A LOT BY
WATCHING
• Feasability & Practicality
are important
‘WE MADE TOO MANY WRONG
MISTAKES’
• Something will go
wrong – be prepared
ARE GOING, YOU MIGHT WIND UP
SOMEPLACE ELSE’
‘IN THEORY THERE IS NO
DIFFERENCE BETWEEN THEORY
AND PRACTICE. IN PRACTICE
THERE IS’
Data Analysis
‘IF YOU CAN’T EXPLAIN IT
SIMPLY- YOU DON’T
UNDERSTAND IT WELL’ ENOUGH’
-ALBERT EINSTEIN
Analytic Methods
• Summarized Data
– Ex. Mean, median
– Treat as a single response then analyze with ANOVA,
regression, etc
– Simplest
– Controversy over how to handle missing data
• Slope
– Single summary measure (variable over time)
– May miss nuances/can’t adjust for other variables
Analytic Methods
• Paired T-test
– Limited to two observations
• (second – first or vis- versa)
• Other Summary Measures:
– Area under the curve (AUC), maximum values
• Disadvantages:
– Missingness can make unreliable
– Reduced statistical power
– If non-linear-difficult to interpret results
Summarized Data
•
WK 6 Fatigue severity correlated with:
radiation dose to the pineal gland (r = 0.86, p =
.07), and altered sleep, including self report
sleep (r= 0.849, p =.016), and as determined
by ACT (r = 0.70, p =.07).
Mean dose pineal gland (Gy)
18
BFI worst
fatigue right now 4
at week 6
7
10
Total
•
Change in melatonin (MLT) levels strongly
correlated with the change in fatigue score (r =
0.90, p = .036), and change in wake time after
sleep onset (WASO) by ACT (r = 0.97, p = .033).
Fatigue severity at WK 6 was also correlated
with the severity of reported neurologic (r =
0.72, p = .043) and cognitive symptoms (r =
0.94, p = .01) at WK 6.
•
Pilot study characterizing change in circadian
pattern of melatonin production
demonstrated ‘shift in melatonin to earlier in
the day & excess production
2
28
50
52
60
Total
11
0
0
0
0
1
0
1
0
0
0
1
0
0
1
1
0
2
0
0
0
0
1
1
1
1
1
1
1
5
Model of Radiation-Induced Fatigue
(Armstrong & Gilbert, 2012)
‘The most
important thing
is not to stop
questioning.
Curiosity has its
own reason for
existing’
-Albert Einstein
Analytic Methods
• Time-by-Time Analysis
– Single or several time points while ignoring the
others
– Useful if finding what timepoint is significantly
different
– Advantage: Missing at other time points do not
impact data; simple
– Disadvantage: increased chance of Type 1 error,
must exclude if missing at needed time point;
complicated analysis (may need to summarize)
RTOG 0525
Testing of Deterioration Status from Baseline
to prior to cycle 4 in MDASI-BT using MID
Set Minimally Important Difference
Classify patients as ‘deteriorated’ or ‘not’
Assess Difference in Proportion in each group
Arm 1
Arm 2
Deterioration
Deterioration
Component
n
%
n
%
p-value*
Symptom
5
10
11
27
0.03
Interference
7
14
13
32
0.03
--Activity
related
-- Mood
related
8
16
15
39
0.01
12
24
12
30
0.49
Median and range in Arm 2
Deterioration:
Overall Symptom change
(1.6; range 1-2.8),
Overall Interference
(2.5; range 1.5-7.7)
Activity Interference
(1.5; range 1.0-8.0)
Example Symptom Burden on RTOG 0825
Using Grouped Data
Improved Deteriorated or No Change
Baseline to Specific Time Points
Significant Less Improvement/More Deterioration in Bev Arm
Wk 10
No Difference
Wk 22
Treatment Factor
(p=0.05)
Wk 34
Treatment (p=0.008)
Affective (p=0.04)
Generalized (p=0.02)
Cognitive (p=0.05)
RTOG 0825
MDASI-BT Baseline to Week 34
More Deteriorated on Bevacizumab
More Improved on Placebo
Analytic Methods
• Mixed or Random Effects
– Types of Analysis:
• Linear mixed effect model
• Mixed effects approach for binary outcome data
• Generalized estimating equations (GEE) approach
• Pro:
– allows evaluation of trends over time using all data points
– Allows evaluation of other variables
• Cons:
– Degree of missingness can impact analysis
– Complicated analysis
MDASI-BT and OS
Early 
Baseline
Cox Proportional Hazards Model for Overall Survival (RPA & MGMT included)
Hazard Ratio
p-value
(95%CI)
Methylation Status (Methylated vs. Not) <.001 2.40 (1.81, 3.18)
RPA (IV vs. III)
RPA (V vs. III)
Baseline Neurologic Factor
0.002
<.001
0.005
1.83 (1.25, 2.66)
3.18 (2.07, 4.88)
1.12 (1.04, 1.21)
Methylation Status (Methylated vs. Not) <.001
2.22 (1.58, 3.12)
RPA (IV vs. III)*
RPA (V vs. III)
Cognitive Factor
1.38 (0.92, 2.09)
2.19 (1.33, 3.60)
1.66 (1.20, 2.29)
0.121
0.002
0.002
Comparative Impact of Treatment on
RTOG 0825
MDASI-BT Longitudinal Trends – P-values
Study Duration (weeks 0-46)
Week
Effect*
Symptom
Inference
WAW
REM
Affective Factor
Cognitive Factor
Neurologic Factor
Treatment Factor
Generalized/disease
Factor
GI Factor
Week/Treatment
Treatment
Interaction
Effect*
Effect*
MGMT
Effect*
RPA
Effect*
0.029
0.180
0.017
0.300
<0.001
0.758
0.601
<0.001
0.891
<0.001
0.443
0.732
0.004
0.747
<0.001
0.664
0.509
<0.001
0.426
<0.001
0.508
0.525
0.038
0.810
<0.001
<0.001
0.143
0.014
0.372
<0.001
0.082
0.017
0.135
0.719
0.003
0.014
0.890
0.029
0.021
<0.001
0.865
0.199
0.011
0.353
<0.001
<0.001
0.124
0.889
0.710
0.041
*Type III test of fixed effects, general linear model (repeated measure), linear trend
Global Symptom
Burden,
Interference &
Multiple Factor
groups
significantly
worse with
Bevacizumab
compared to
Placebo
MDASI-BT Longitudinal Analysis from RTOG 0825
Overall Interference
5
5
4.5
4.5
4
4
P = 0.040
3.5
Inference Score
MDASI Score
Cognitive Factor
3
2.5
2
1.5
P < 0.001
3.5
3
2.5
2
1.5
1
Placebo
1
Placebo
0.5
Bevacizumab
0.5
Bevacizumab
0
0
0
6
10
22
34
Weeks from Randomization
46
0
6
10
22
34
46
Weeks from Randomization
Molecular epidemiology approach to cancer-related symptoms
Published in final edited form as:
Lancet Oncol. 2008 August; 9(8): 777–785.
doi: 10.1016/S1470-2045(08)70197-9
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3390774/figure/F2/
When you come to a
fork in the road – take
it
-Yogi Berra
Upcoming Peaks
Grant# 1 R01 NR013707-01A1;
Symptoms-Toxicity-Response
Electronic Data Capture
www.cern-foundation.org
Study Publication
‘IT AIN’T OVER TIL IT’S OVER’
-YOGI BERRA
Summary
• Planning is key
• Seek input
• Analysis plan dependent on question of
interest
• Integrated analysis to fully understand the
symptom (molecular epidemiologic approach)
• Publication of results!
Special Thanks to the patients and families
Who participated in these trials
Success is not final, failure is not fatal – it is
the courage to continue that matters
-Winston Churchill