1. Cohort Studies November 4 2004 Epidemiology 511 W. A. Kukull

2. Cohort Studies Prospective/ Concurrent Retrospective Today The Future The Past Today exclude “Diseased”

3. Retrospective v. Prospective Retrospective –Cohort is Reconstructed by the investigator –Both exposure and outcome occur before study begins –Depends on record quality Prospective –Cohort is Established by investigator –Outcomes occur after study begins Loss to follow up is the major threat to validity for both types of cohort study

4. Cohort Types (not seen in Gordis) Closed or Fixed –a fixed set of persons followed from starting point to endpoint –most common in short term studies Open or Dynamic –Additional persons can enter with time –a subject can contribute person-time to several exposure and confounder categories

5. Design a cohort study: does HDL cholesterol level decrease risk of stroke? Population: –Reference or base population –Who will be included? How sampled? –How many to include? Disease incidence? Exposure frequency? Follow-up time: will we have enough cases to analyze by the end of our “grant cycle”?

6. Enrollment in the cohort: Non- Participation During Cohort Formation Those who agree to participate may differ importantly from those who do not agree Affects Generalizability of association –is the “risk” found by the study likely to apply to non participants, Do exposure profiles differ? –Incidence estimates may be affected Does not usually affect validity of association –population disease/exposure rates may be underestimated but the observed association will be valid

7. Non-Participation (2) If non-participants are both exposed and are independently at high (or low) disease risk, then a biased estimate of association (e.g., RR) If non-participation is related only to exposure or only to outcome, the association will be valid but power may be reduced. IN CONTRAST: non-participation is a major source of bias in Case-control studies or prevalence studies

8. Increase response rates Mail Surveys –Multiple mailing Telephone interviews –Interviewer experience In-person Interviews –Home –Clinic –Other

9. Starting with Non-Diseased: How do we exclude persons with stroke hx? Medical Records Employment records Personal Interview Screening test; biomarkers Full Clinical exam –lab and radiology(CT/MRI), –Hx, PE

10. Natural Hx of Disease generally speaking… PreClinical Phase Biologic onset of disease Clinical Phase Sx Dx Rx Outcome

11. Which exposures are relevant to disease onset? Too close to onset, disease already active Exposure level may fluctuate Too distant exposures may not be possible to cause onset Changes in exposure during f/u Know as much as you can about the disease and potential mechanism of exposure action

12. Exposure and Onset: how we think of onset influences potential relevant exposures Latent period Induction Initiator/ promotor exposure Biologic Onset Disease Detectable by Screen Sx Outcome DPCP DPCP = Detectable preclinical phase

13. “Exposures” in your cohort? Variables of Interest: For example… HDL/LDL TriG Diet Medications, etc. Confounders: For example… Age Education Gender HTN, Diabetes, A-fib smoking Effect Modifiers For example… Genotype Gender Comorbidity Age etc. N.B. exposures above may be in any category, depending on Your research question and available data

14. How will exposure information be obtained at baseline and F/U? Biological measurement (e.g., HDL) Questionnaire Self/proxy-report interview Clinic/hospital records Will data collected at f/u be different from baseline? –Blood samples; exams; interviews

15. Defining Follow-up How often will the cohort be “followed” –Continuous surveillance possible? –Monthly, yearly, every 3 years –What is feasible re: staff How will “disease” occurrence be counted? –Onset at f/u visit? Onset between visits? –Impact on person-time to disease onset

16. Stroke happens! Finding the incident cases from the cohort Establish firm definition of “case” –Ischemic or hemorrhagic stroke First occurrence? –Clinical criteria; confirmed by imaging? –Pathology; death certificate Records, interview, examination Regular intervals or continuous surveillance Age at onset (or accumulated person-time)

17. Measurement Error: information bias (White et al,1998) Errors in Selection or design of instrument Errors in instrument use Poor execution of protocol Inherent subject characteristics Drift in accuracy over time Errors in data processing

18. Reducing Measurement Error Select a Cohort expected to provide accurate exposure data Select a cohort with greater range of exposure. Use repeated measurements of exposure Quality control/reliability & validity Drop cases that occur close to exposure measurement time

19. Retention and Tracking of the Cohort (Hunt & White,1998) Enrollment: willingness, commitment, study requirements Bonding: newsletters, cards, study theme Regular contact (6 – 24 mo) –Tracking system; primary outcomes Dedicated staff Incentives: like cash or coffee mugs

20. Loss to follow-up Major source of bias in cohort studiesMajor source of bias in cohort studies –What if diseased subjects are lost? –What if exposed persons are lost? –What if 40% of the cohort drops out? What to do if it happens? –estimate best and worst case scenarios ( if all lost subjects did or did not develop outcome) Large losses may invalidate the study

21. Example (See, Hennekens & Buring) Consider British Physicians study –~66% response rate in cohort formation –Heaviest smokers 20 times more likely to die of lung cancer –Was result invalid due to high non-response? –To obtain a “null” smoking effect: smoking would have to have been a 30-fold protective factor in the non-respondents

22. Analysis Relative Risk –Cases/person-years (incidence density RR) –Cases per enrolled subjects (cumulative incidence by end of study period: RR) Crude analysis Stratified analysis Multivariate analysis –Logistic, Poisson and Proportional Hazards

23. Analysis: HDL and Stroke Is baseline HDL level associated with stroke occurrence during the study period? –Continuous measure of HDL –Categorical: high/med/low Which other factors are also associated with stroke? Do they mix with the HDL effect? –Stratified analysis; multivariate analysis Does HDL effect vary by levels of another factor? –Interaction, effect modification –Stratified analysis; multivariate analysis

24. Cohort Study: strengths Useful when Exposure is rare Can examine multiple outcomes of a single exposure Describes Temporal relationship between exposure and disease If prospective, minimizes exposure determination bias Allows direct measurement of incidence

25. Cohort Study: limitations Usually Inefficient for rare diseases Prospective: usually expensive and long Retrospective: requires good records Validity of the results can be seriously affected by losses to follow-up

26. Cohort v. Case-Control Suppose 8 well-designed case-control studies consistently found an elevated odds ratio--- Would a Cohort study be warranted? What qualities would we want to have in cohort study enrollees? What sources of bias are more likely in each study design?