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Cohort Studies Part I

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					   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      type)
•   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)
            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 non-
exposed 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 follow-
up.
    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-         IncidenceE+ = 54/458 = 0.118
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)

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
         Actual                        Scenario 1
        D+     D-                       D+     D-
 E+     54     404    458       E+      114    404        518
 E-     44     398    442       E-      48     434        482
                      900                             1000

IncidenceE+ = 54/458 = 0.118   IncidenceE+ = 114/518 = 0.220
IncidenceE- = 44/442 = 0.100   IncidenceE- = 48/482 = 0.100
RR = 0.118 / 0.100 = 1.18      RR = 0.220 / 0.100 = 2.21
95%, C.I. = (0.81, 1.72)       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
        Actual                        Scenario 2
        D+   D-                        D+    D-
 E+     54  404       458       E+     66   452           518
 E-     44     398    442       E-      48     434        482
                      900                             1000

IncidenceE+ = 54/458 = 0.118   IncidenceE+ = 66/518 = 0.127
IncidenceE- = 44/442 = 0.100   IncidenceE- = 48/482 = 0.100
RR = 0.118 / 0.100 = 1.18      RR = 0.127 / 0.100 = 1.28
95%, C.I. = (0.81, 1.72)       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 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
        Actual                        Scenario 3
        D+   D-                        D+    D-
E+      54  404       458       E+     57   461           518
 E-     44     398    442       E-      48     434        482
                      900                             1000

IncidenceE+ = 54/458 = 0.118   IncidenceE+ = 57/518 = 0.110
IncidenceE- = 44/442 = 0.100   IncidenceE- = 48/482 = 0.100
RR = 0.118 / 0.100 = 1.18      RR = 0.127 / 0.100 = 1.11
95%, C.I. = (0.81, 1.72)       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                     RR = 1.11
95%, C.I. = (0.90, 1.82)      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 follow-
 up (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 non-
     participation, 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?

				
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