Unit 8:
Cohort Studies
Unit 8 Learning Objectives:
Considering the prospective cohort study:
1. Understand strengths and limitations of this
study design.
2. Understand approaches to selecting an
“exposed” population.
3. Understand approaches to selecting a
comparison group(s).
4. Recognize primary sources of exposure and
outcome information.
Unit 8 Learning Objectives:
Considering the prospective cohort study:
5. Recognize contributions of major studies
conducted in the United States.
--- Framingham Heart Study
--- Nurses Health Study
6. Understand primary sources of bias.
7. Understand the purpose and methods for
conducting sensitivity analyses.
Unit 9 Learning Objectives:
8. Understand design features and strengths
and limitations of retrospective cohort
studies.
9. Differentiate between incidence risk and rate,
and risk ratio and rate ratio.
10. Calculate person time for “time-dependent”
exposures.
11. Understand factors that influence accurate
classification of person-time exposure.
12. Understand the concept and components of
the “empirical induction period.”
13. Understand the concept of “non-exposed
person-time” among “exposed” subjects.
Axiom:
Since most epidemiologic research is
“observational” by nature, epidemiologic
studies typically obtain imprecise
answers, but to the right health-related
questions that cannot be evaluated using
experimental study designs.
Prospective
Cohort
Study
Review – Prospective Cohort Study
Prospective cohort (“follow-up”) study:
• Disease free individuals are selected and their
exposure status is ascertained.
• Subjects are followed for a period of time to
record and compare the incidence of
disease between exposed and non-exposed
individuals
(e.g. risk ratio or rate ratio).
Review – Prospective Cohort Study
Prospective cohort (“follow-up”) study:
Exposure
Disease
?
?
Exposure may or may not have occurred at
study entry
Outcome definitely has not occurred at study
entry
Prospective
Cohort Studies
(Also called “longitudinal” studies)
Design Features
Strengths:
• Can elucidate temporal relationship between
exposure and disease (hence, “strongest”
observational design for establishing cause and
effect).
• Minimizes bias in the ascertainment of
exposure (e.g. recall bias).
• Particularly efficient for study of rare
exposures.
Design Features
Strengths (cont.):
• Can examine multiple effects of single
exposure.
• Can yield information on multiple exposures.
• Allows direct measurement of incidence of
disease in exposed and non-exposed
groups
(hence, calculation of relative risk).
Design Features
Limitations:
• Not efficient for the study of rare diseases.
• Can be very costly and time consuming.
• Often requires a large sample size.
• Losses to follow-up can affect validity of results.
• Changes over time in diagnostic methods may
lead to biased results.
Design Features
Selection of the Exposed Population:
The exposed population should relate to the
hypothesis:
• For common exposures (e.g. smoking, coffee
drinking) and relatively common chronic
diseases, the general
population/geographically-defined
areas are
good choices.
• For rare exposures, ”special cohorts” are more
desirable (e.g. particular occupations or
environmental factors in specific geographic
locations).
Design Features
Selection of the Exposed Population:
• Although cohort studies are not optimal for
evaluation of rare diseases, certain
outcomes may be sufficiently common in
”special exposure
cohorts” to yield an
adequate number of cases.
• To enhance validity, some exposed populations
are selected for their ability to facilitate complete
and accurate information (e.g. doctors, nurses,
entire companies, etc.).
Design Features
Selection of the Comparison Group:
• The groups being compared should be as
similar as possible on all factors that relate to
disease other than the exposure under
investigation (e.g. to reduce the potential for
confounding).
• Ability to collect adequate information from the
non-exposed group is essential.
Design Features
Internal Comparison Group:
• Members of a single general cohort are
classified into exposed and non-exposed
categories.
• Most often used for common exposures.
• The non-exposed group becomes the
comparison group.
• Must be careful of other potential differences
between the exposed and non-exposed
groups.
Design Features
General Population Comparison Group:
• The general population will probably include
some exposed persons.
• Due to the “healthy worker effect,” the general
population may be expected to experience
higher mortality than most occupational cohorts.
• Comparisons with population rates are
possible only for outcomes for which
population rates are available.
Design Features
Special Exposure Comparison Group:
• Another cohort with demographic
characteristics similar to the exposed
group, but considered non-exposed to the factor
of interest is selected (e.g. another occupational
group).
Note: To enhance validity, it may be important to
have multiple comparison groups.
Design Features
Sources of Exposure Information:
•
Pre-existing Records:
Advantages:
---
Inexpensive
---
Relatively easy to work with
---
Usually unbiased since the data were
collected for non-study purposes
Design Features
Sources of Exposure Information:
•
Pre-existing Records:
Disadvantages:
---
Exposure information may not be
precise enough to address the
research question.
---
Records frequently do not contain
data on potential confounding factors.
Design Features
Sources of Exposure Information:
•
Self Report (interviews, surveys, etc.)
Advantages:
---
Opportunity to question subjects on
as many factors as necessary.
---
Good for collecting information on
exposures not routinely recorded.
Design Features
Sources of Exposure Information:
•
Self Report (interviews, surveys, etc.)
Disadvantages:
---
Subject to response bias (e.g. due to
stigma, response expectations, etc.).
---
Subject to interviewer bias.
---
Subjects may be sufficiently unaware
of their exposure status (e.g.
chemical exposure).
Design Features
Sources of Exposure Information:
•
Direct Measurement
If obtained in a comparable manner, can
provide objective and unbiased exposure
ascertainment (e.g. blood pressure, serum
samples, environmental measurements,
etc.).
---
Can be used on a fraction of the
cohort to validate other types of
exposure ascertainment.
Design Features
Sources of Exposure Information:
•
Repeated Measurements
-- If frequency of exposure changes over
follow-up, repeated measurements allows
for revision of exposure classification.
--- Periodic questioning of cohort members
allows for newly identified exposures of
interest to be measured.
--- Good for “transient” exposures.
Design Features
Types of Exposure Measurements:
•
Dichotomous (e.g. presence of HLA
•
Intensity (e.g. mean blood pressure level)
•
Duration (e.g. weeks of chronic stress)
•
Cumulative (e.g. pack-years of smoking)
•
Regularity (e.g. frequency of episodic anger)
•
Variability (e.g. range of cardiovascular
reactivity)
type)
Design Features
Sources of Outcome Information:
•
Death certificates (National Death Index) –
for some causes, notoriously unreliable
•
Clinical history
•
Self-reports
•
Medical examination (periodic
re- examination of the cohort)
•
Hospital discharge logs
Design Features
Outcome Information:
• Procedures for identifying outcomes must be
equally applied to all exposed and nonexposed individuals.
• Goal is to obtain complete, comparable, and
unbiased information on the health
experience of each study subject.
• Combinations of various sources of outcome
data may be necessary.
Prospective Cohort Study
Examples:
•
Framingham Heart Study
•
Nurses Health Study
Prospective Cohort Study
Framingham Heart Study:
• Framingham, MA (1948):  5,000 of the 30,000
town residents ages 30 to 59 years of age
without
established coronary disease
participated.
• “Exposures” include smoking, obesity, elevated
blood pressure, high cholesterol, physical
activity, and others.
• “Outcomes” include development of coronary
heart disease, stroke, gout, and others.
Prospective Cohort Study
Framingham Heart Study:
• Outcome events were identified by examining
the study population every 2 years, and by
daily surveillance of hospitalizations in the only
hospital in Framingham, MA.
• Participants followed for more than 30 years.
• Study has made fundamental contributions to
our understanding of the epidemiology of
cardiovascular disease.
Prospective Cohort Study
Framingham Heart Study:
• More than 200 published reports.
• Unfortunately, Framingham, MA is almost
exclusively Caucasian.
Prospective Cohort Study
Nurses Health Study:
• In 1976, > 120,000 married female nurses ages
30 to 55 in one of 11 U.S. states
participated.
• At 2-year intervals, follow-up questionnaires
were completed on development of
outcomes and exposure information.
• “Exposures” include use of oral
contraceptives, post-menopausal hormones,
hair dyes, dietary fat consumption, age at first
birth, and others.
Prospective Cohort Study
Nurses Health Study:
• “Outcomes” include heart disease, various
types of cancer, and others.
• Many new “exposures” have been added to the
biennial questionnaires (e.g. electric blanket use,
selenium levels, etc.).
Prospective Cohort Study
Follow-up Issues:
•
Major challenge is to collect follow-up data
on every study subject.
•
Loss to follow-up is a major source of bias
and is related to:
•
---
Length of follow-up
---
Monitoring methods used in the study
Multiple sources of information can be
used to obtain complete follow-up information.
Prospective Cohort Study
Sources of Error (Bias):
Loss to Follow-up:
• If large (e.g. > 30%), validity of study results
may be severely compromised.
• Probability of loss to follow-up may be related
to
exposure, disease, or both – this may lead
to a biased exposure/disease estimate.
• Can use “sensitivity” analysis to estimate
potential effect of subjects lost to followup.
Prospective Cohort Study
Sensitivity Analysis:
General Definition:
• Substitution of a value or range of values to
evaluate the robustness of study findings,
while taking into account the potential impact of
study limitations.
For example, how might the final outcome of
the analysis change when taking into
account loss to follow-up?
Prospective Cohort Study
Sensitivity Analysis (Example):
Prospective cohort study of lumber mill
occupation and low back pain.
1,000 subjects recruited
---
518 exposed (lumber mill workers)
---
482 non-exposed (other workers)
100 of 1,000 lost to follow-up
---
60 exposed, 40 non-exposed
Sensitivity Analysis
D+
D-
E+
54
404
458
IncidenceE- = 44/442 = 0.100
E-
44
398
442
RR = 0.118 / 0.100 = 1.18
900
95%, C.I. = (0.81, 1.72)
IncidenceE+ = 54/458 = 0.118
Possible Scenarios from loss to follow-up:
Scenario 1 (Extreme): All 60 exposed lost to
follow-up experienced low back pain, whereas the
rate in the 40 non-exposed lost to follow-up was
same as those with complete follow-up.
Sensitivity Analysis
Scenario 1
Actual
E+
D+
54
D404
458
E-
44
398
442
900
IncidenceE+ = 54/458 = 0.118
IncidenceE- = 44/442 = 0.100
RR = 0.118 / 0.100 = 1.18
95%, C.I. = (0.81, 1.72)
E+
D+
114
D404
518
E-
48
434
482
1000
IncidenceE+ = 114/518 = 0.220
IncidenceE- = 48/482 = 0.100
RR = 0.220 / 0.100 = 2.21
95%, C.I. = (1.61, 3.03)
Sensitivity Analysis
Possible Scenarios from loss to follow-up:
Scenario 2 (Possible): The incidence of the 60
exposed lost to follow-up is twice the rate of
the incidence of the 40 non-exposed lost to
follow-up.
The incidence of the 40 non-exposed lost to
follow-up is the same as the incidence of the
442 non-exposed in the study.
Sensitivity Analysis
E+
Actual
D+
D54
404
458
E+
E-
44
442
E-
398
900
IncidenceE+ = 54/458 = 0.118
IncidenceE- = 44/442 = 0.100
RR = 0.118 / 0.100 = 1.18
95%, C.I. = (0.81, 1.72)
Scenario 2
D+
D66
452
48
518
434
482
1000
IncidenceE+ = 66/518 = 0.127
IncidenceE- = 48/482 = 0.100
RR = 0.127 / 0.100 = 1.28
95%, C.I. = (0.90, 1.82)
Sensitivity Analysis
Possible Scenarios from loss to follow-up:
Scenario 3 (Possible): The incidence of the 60
exposed lost to follow-up is half the rate of the
incidence of the 40 non-exposed lost to followup. The incidence of the 40 non-exposed lost
to follow-up is the same as the incidence of the
442 non-exposed in the study.
Sensitivity Analysis
E+
Actual
D+
D54
404
458
E+
E-
44
442
E-
398
900
IncidenceE+ = 54/458 = 0.118
IncidenceE- = 44/442 = 0.100
RR = 0.118 / 0.100 = 1.18
95%, C.I. = (0.81, 1.72)
Scenario 3
D+
D57
461
48
434
518
482
1000
IncidenceE+ = 57/518 = 0.110
IncidenceE- = 48/482 = 0.100
RR = 0.127 / 0.100 = 1.11
95%, C.I. = (0.77, 1.59)
Sensitivity Analysis
Actual
Scenario 1
RR = 1.18
RR = 2.21
95%, C.I. = (0.81, 1.72)
95%, C.I. = (1.61, 3.03)
Scenario 2
Scenario 3
RR = 1.28
95%, C.I. = (0.90, 1.82)
RR = 1.11
95%, C.I. = (0.77, 1.59)
With 10% loss to follow-up, the observed risk ratio
estimate of 1.18 appears to be robust with regard to
possible (but not extreme) impact of loss to followup (e.g. Scenarios 2 and 3).
Sensitivity Analysis
Note: Even if loss to follow-up is low (e.g. 10%),
if the incidence is very low in the observed study
population (e.g. < 5%), yet relatively high in those
lost to follow-up (e.g. > 15%), the observed point
estimate may be severely biased…..
e.g. because of loss to follow-up, you missed “all
of the action” (where the cases occurred).
Prospective Cohort Study
Sources of Error (Bias):
Misclassification of Exposure and/or Outcome:
• Random (non-differential) misclassification
• Non-random (differential) misclassification
• Can use “sensitivity” analysis to estimate
potential effect of postulated degree(s) of
misclassification.
Prospective Cohort Study
Non-Participation:
• Participants often differ from non-participants
in important ways.
• A “valid” result will not be affected by nonparticipation, although generalizability may
be affected.
• True exposure/disease relationship will be
biased if non-participation is related to both the
exposure and other risk factors for the
outcome under study.
Review of Recommended Reading
CRP, LDL, and First CVD Events
--- Prospective cohort study within an randomized trial of
27,939 apparently healthy American women (1992-95)
in the Women’s Health Study (WHS).
--- WHS is an ongoing evaluation of aspirin and vitamin E
for primary prevention of CVD events among women
>45 yrs.
--- Before randomization, blood samples collected and
stored with assays performed for CRP and LDL.
--- First CVD event defined as non-fatal MI, non-fatal
ischemic stroke, coronary revascularization, and death
from cardiovascular causes.
--- Participants followed for average of 8 years.
--- Analyses conducted separately by HRT status.
Discussion Question 1
Interpret results from figure 1 and table 2.
Among CRP and LDL cholesterol at baseline,
which variable seems to best predict
the risk of cardiovascular disease
over 8 years of follow-up?
Source: NEJM 2002; 347:1557-1565.
Discussion Question 2
Interpret the results from table 3.
For risk estimates associated with CRP, is there
evidence of effect measure modification
by hormone replacement therapy status?
What about the risk estimates for LDL?
Discussion Question 3
Interpret the results from figure 3 and 4.
Do baseline levels of CRP and LDL
cholesterol independently predict subsequent
cardiovascular risk, or do they simply measure
a common (shared) domain of risk?