Case-Control Studies

1. Case-Control Studies November 18 2004 Epidemiology 511 W. A. Kukull

2. History • Lane-Claypon (1926) first case control study: reproductive experience and breast ca • Sociology used case control methods in 1920’s and 30’s • Wynder& Graham (1950) and others linked cigarette smoking to lung ca • Cornfield (1951) direct standardization to control confounding • Mantel and Haenszel (JNCI, 1959) stratified analysis

3. Case Control Studies Not Exposed Hx of Exposure Hx of Exposure Not Exposed Cases (with Disease) Controls (no Disease)

4. Population-based, Incident, Case-Control Study Study Base or Population All New Cases of Disease “X” that meet study criteria “Sample” of persons without Disease “X” (Controls) “Controls” must be able to become diseased and must be sampled without regard to “exposure”.

5. “Study base” concepts Eligible Subjects Case - Control Bias? Loss, death, refusals before disease develops Cohort Study enrollees Enroll all? Disease cases Non- diseased Sample? Follow-up Time

6. Nested Case Control Use data or biomarkers collected at cohort entry to determine exposure status for cases and controls Cohort study population: Exposed and Unexposed Developed disease Did Not develop Disease “Controls” Sampled or Selected from the remaining unaffected when each case is diagnosed Cases

7. Selecting Cases • Must cases be “representative” of all persons who have the disease? • What about female cases? • Severe cases? • early onset cases? • Cases from Omaha? • They must be selected independent of exposure !! (and define a study base)

8. Principles of Comparability(after Wacholder et al, AJE;1992;135:1019-28) • Case-control comparisons should be made within subjects from the same study base; (selection bias) • Effects of other factors on the disease-exposure association being studied should be minimized; (confounding) • Errors in exposure measurement should be non-differential; (information bias)

9. Case – Control comparability(after Koepsell & Weiss, 2003) • Comparability and “representativeness” • If each of the study cases had not developed the disease, would they have been included in the study base/population? • If each of the non-cases in the study base/population had developed the disease, would they have been included as a case? • Can we characterize the study base/population?

10. Representativeness?Ambiguous interpretation • Cases may be restricted to any type of case • The case definition will define the “study base” or source population for controls • Cases do not need to be a “random sample” of the entire diseased universe to be valid • Case selection and inclusion criteria will affect the research questions that can be answered

11. Selecting Cases • Disease criteria: clinical or histopathological evidence? • Hospitalized cases or cases from a registry? • may need to include more than one hospital • Incident or Prevalent cases ? • survival bias? Prevalent case sample may miss persons dying early in the course of disease • Health services factors plus risk factors

12. Cohort design similarities • Suppose we could find all new cases of ALS in a particular town as they occur • Could we conduct a cohort study ? • We know the population of the town • How would we measure exposures for everyone? • Could we take a “sample” of the persons without ALS and compare them to cases

13. Sources of Controls:Are they part of the same study base? • Community or Population-based • RDD • Friend or spouse • Neighborhood • Hospitalized patients • unrelated to exposure; multiple diagnoses • Medicare and government lists • HMO enrollees

14. Timing • Specify a reference time • e.g. diagnosis for cases, similar time for control • Determine exposure before reference time • later ones don’t count • What if enrolled controls later get the disease of interest? • Controls at enrollment are compared to cases at enrollment

15. Exposure and Onset: how we think of onset influences potential relevant exposures Potentially effective Exposures (Critical period) Biologic onset of disease Sx/Dx Outcome Disease Detectable by Screen Irrelevant Exposures ???

16. Comparable exposure periods for controls? Setting a “reference age/time” Case age at biologic onset of disease Potentially effective Exposures (Critical Period) Case age at Sx/Dx Outcome Age/time Disease first Detectable by Screen Irrelevant Exposures ???

17. Proxy RespondentsWhen the subject can’t respond • Spouse, sibling, child, friend • Use for both case and control • when proxies systematically over or underestimate exposure • when control responses and their proxies are poorly correlated • when there is no information relating proxy and subject responses

18. Choosing controls (1) • We want to study computer use as a risk factor for carpal tunnel syndrome among 16 yo women • We find cases through the hospital neurology clinic • We enroll the best friend of each case (same age and sex), who has no symptoms, as a control—for a paired case-control design

19. Choosing controls (2) • We want to study the effect of smoking on carotid artery stenosis/occlusion • Cases selected from UWMC vascular clinic • carotid doppler > 70% stenosis • Controls are selected from UWMC pulmonary clinic • carotid doppler : <20% stenosis • Determine smoking Hx for each • What would we expect to find

20. Choosing controls (3) • Selection Bias • controls representative of the study base? • selection related to exposure hx? • Information bias (recall and other) • is the exposure measured with the same accuracy in cases as in controls • Residual confounding: unmeasured factors • Statistical power

21. Example: study base(after MacMahon and Trichopoulos) • Case control study of induced abortion and subsequent ectopic pregnancy • Cases: 26 women with EP and one previous pregnancy • 3 controls for each case from same maternity hospital (matched on age, education and pregnancy order) • Result : odds ratio of 10.0

22. Example: IA an EP (2) • Were controls representative of the case “study base”? • Controls were generally completing pregnancy (in general, women completing were less likely to have had an IA) • Cases of EP is diagnosed early in gestation • Later study, with only “new” pregnancy controls showed OR= 1.9, not 10.0

23. Measuring Exposure • Recall Bias • Are cases more/less likely to recall exposure than controls? • Limitations of recall • Is the person’s recall valid? • Comparability in cases and controls • Validity (accuracy) of measures

24. Comparability of Exposure Information • Does incompleteness or inaccuracy occur to a different degree in cases vs controls? • Same degree? • What would happen to the Odds Ratio in either case? • attenuation (reduction toward null value)? • exaggeration? • spurious association?

25. Comparability Considerations: Exposure Measurement • Provisions for “sensitive” questions (illicit drug use, sex, income) • Biologic specimens: lab methods, storage degradation • Excessively long interviews • psychological testing • food frequency • occupational and extended family history

26. Obtaining ComparabilityExposure measurement • Keep staff unaware of hypothesis and case/control status • Place and circumstances of interview • equal proportions of cases and controls interviewed by each staff member • Use information recorded prior to time of diagnosis (hospital or pharmacy records) • Direct Measurement (EMF, radiation)

27. Evidence of Comparability • Is there similar proportions of “missing” data in cases and controls? • Does the time duration of interviews differ? • Ask about etiologically irrelevant characteristics and assess response • e.g. if studying pancreatic ca and coffee ask also about tonsillectomy and hemorrhoids • More than one source

28. ExampleTrue classification MI No MI Illegal drug 135 50 No Illegal drug 95 180 230 230 OR= 5.1

29. Example:Reporting accuracycases .90; controls .20Differential misclassification No MI (control) MI (case) Illegal drug 112 10 No Illegal drug 118 220 OR = 20.8 230 230

30. Example:Reporting accuracycases .20; controls .20Non-Differential misclassification No MI (control) MI (case) Illegal drug 27 10 No Illegal drug 203 220 230 OR = 2.9 230

31. Example summary • Differential misclassification may bias Odds Ratio in either direction • Non Differential misclassification usually biases toward the NULL (1.0) • under some circumstances it may be biased away from the null • Don’t always trust it to underestimate true effect

32. Example: Recall Bias Comparability of information • We are studying prenatal maternal infection and congenital malformations. • True incidence of infection is 15% for both cases and controls • IF Case mothers recall 60% or their true infections; controls recall 10% of theirs • Results will show 9% infection rate in cases and 1.5% among controls (OR ~ 6.0)

33. Matchingto reduce potential confounding by design • Individual matching requires a “matched” analysis • pairs are the unit of analysis OR= b/c • Frequency matching uses a standard, stratified or unmatched analysis • May require that cases be enrolled before controls

34. Indications for Matching • If the unmatched groups have little overlap on the factor (and the factor is associated with disease) • Small studies of rare diseases with several confounding variables • To account for unmeasured confounders, through a surrogate measure, e.g., neighborhood • Especially, when the matched factor is a STRONG confounder

35. MatchingPotential problems • Finding a match for the 93 y/o man from Ballard, who was a fisherman, is married, has five children, and drinks socially • Age, neighborhood, occupation, marital status, offspring, alcohol use—too much matching? • Once you have “matched” on a factor you cannot study that factor • Why? because we have artificially established equal proportions of that factor in the cases and the controls

36. Overmatching • Matching on a variable intermediate in the causal pathway • Suppose smoking alters cholesterol, and cholesterol is associated with CVD • What if we matched on cholesterol? • Don’t match on factors related to exposure of interest but not to disease • Contraceptives, religion, -> embolism

37. Case Control Studies:Limitations • Inefficient when exposure is rare • Cannot compute incidence rates directly • Sometimes difficult to establish temporal relationship between exposure and disease • Prone to biases: • Selection bias • Information bias, specifically RECALL bias

38. Case Control Studies:Strengths • Relatively quick and inexpensive • Good for diseases with long latent periods • Optimal for RARE diseases • Can examine multiple etiologic factors for a single disease

39. Conclusion • Define a study base • Select diseased and non diseased persons • Measure history of “exposure” • Compare exposure hx in cases and controls • Assess possibility of bias • misclassification and non-comparability of exposure data • inappropriate study base sampling; timing