1. Study Design: Case-Control Studies Paul L. Reiter, PhD Assistant Professor Division of Cancer Prevention and Control Paul.Reiter@osumc.edu

2. Learning Objectives  Describe the strengths and weaknesses of case-control studies  Describe the importance of the selection of controls  Compare and contrast the different types of matching for case-control studies  Describe the different types of biases commonly associated with case-control studies

3. Module Outline  Case-Control Studies  What are they?  Case Selection  Control Selection  Matching  Potential Biases  Strengths and Weaknesses

4. Evidence Pyramid

5. 2 x 2 Table ab cd Disease Exposure Yes No YesNo

6. Case-Control Study  Nature / subjects / others assign exposure status  No formal procedure of random assignment  Subjects selected based on disease status (cases and controls)  Past exposure status is determined for cases and controls  Compare exposure in cases versus controls

7. Case-Control Study ab cd Disease Exposure Yes No YesNo Case-control studies start with disease status and then determine exposure

8. Module Outline  Case-Control Studies  What are they?  Case Selection  Control Selection  Matching  Potential Biases  Strengths and Weaknesses

9. Source Cohort “It is helpful to think of any case-control study as being nested – that is, conducted – within a cohort of exposed and unexposed…Case-control studies can be thought of as nested within a source population…” Rothman and Greenland

10. Source Cohort  The “source cohort” behind a case-control study is the population (cohort) that gave rise to the cases  Example Cases: lung cancer cases in Franklin County, OH Source Cohort: residents of Franklin County, OH

11. Selecting Participants ??? Disease Exposure Yes No YesNo Goal: Select n 1 cases and n 0 controls from source cohort without knowledge of their exposure status n1n1 n0n0

12. Selecting Cases  We want cases to be all (or a “representative” sample) of the diseased members of the source cohort  “Representative” = group provides a valid estimate of exposure

13. Time Source Cohort Cases Controls Study Cases Selecting Cases

14. Where to Find Cases  Clinic-based cases  Hospitals  Outpatient clinics  Physician practices  Population-based cases  Disease registries  Death certificates

15. Considerations  Clinic-based cases  Possibly harder to define “source cohort” due to referral patterns  If examining severely ill patients, may get “survivors” instead of a representative sample  Population-based cases  May be difficult to find a registry for some diseases (e.g., HPV infection)

16. Incident vs. Prevalent Cases  Incident  Newly diagnosed cases  Have to wait for new cases to be diagnosed and have system for identifying them  Prevalent  People who may have had disease for some time  Any risk factors identified may be related more to survival than disease development  Verdict: Incident cases are generally preferred

17. Module Outline  Case-Control Studies  What are they?  Case Selection  Control Selection  Matching  Potential Biases  Strengths and Weaknesses

18. Selecting Controls  We want controls to be a “representative” sample of the non-diseased members of the source cohort  “Representative” = group provides a valid estimate of exposure  Selecting controls is extremely important since they serve as the “comparison group” to cases for your study  Want to select the most valid comparison group possible

19. Selecting Controls  Select individuals who might have become cases in your study if they had developed disease, that is, from the source cohort that gave rise to the cases  Try to conceptualize the “source cohort” (although it may not be easily identifiable) and select controls from that cohort

20. Time Source Cohort Cases Controls Study Cases Selecting Controls Study Controls

21. Selecting Controls is Difficult!  Control selection is “one of the most difficult problems in epidemiology” (Gordis)  It is also one of the most important components of a case-control study!

22. Where to Find Controls  Medical care system  Hospitals  Outpatient clinics  Physician practices  Community  General population  Family members or friends  Neighbors (geographic controls)  Other (schools, worksites, etc.)  Deceased individuals

23. Medical Care System Controls  Advantages  Theoretically belong to same source cohort as cases (if using clinic-based cases)  Easily identifiable  High cooperation rate  “Mental set” is similar to cases (potentially less recall bias)  Disadvantages  Might have medical condition caused by exposure  Only a subset of source population

24. Medical Care System Controls  General rules  Choose control conditions likely to have same referral pattern as disease of interest  Exclude conditions known to be associated (positively or negatively) with the exposure  Preferable to select controls from multiple disease categories

25. Community Controls  Advantages  Theoretically belong to same source cohort as cases (if using population-based cases)  Random sampling of population-based controls is usually the most desirable option, if possible  Disadvantages  Source cohort not always easily identifiable to allow for random sampling of controls  Low cooperation rate  Possible “overmatching” if using family or friends  “Mental set” different from cases (recall bias)

26. Community Controls - Methodology  Random digit dialing (RDD)  Cell phone only households  Negative influence of telemarketing  Door-to-door  More likely option for developing countries  Ask cases to provide list of family members, friends, or neighbors  Public databases (DMV, voter registration lists, etc.)

27. How Many Controls Do I Need? 0123456 Precision of Estimates Number of Controls per Case Returns in statistical efficiency diminish drastically by increasing the control to case ratio beyond 4 or 5

28. Module Outline  Case-Control Studies  What are they?  Case Selection  Control Selection  Matching  Potential Biases  Strengths and Weaknesses

29. Matching - Definition “Matching refers to the selection of a reference series – unexposed subjects in a cohort study or controls in a case-control study – that is identical, or nearly so, to the index series [exposed or cases] with respect to the distribution of one or more potentially confounding factors.” Rothman and Greenland

30. Reason for Matching “A major concern in conducting a case-control study is that cases and controls may differ in characteristics or exposures other than the one that has been targeted for the study.” Gordis

31. Matching  Matching basically makes sure that controls and cases are similar on certain characteristics  Two types of matching  Individual matching  Group matching

32. Individual Matching  Also called “match pairs”  Matching occurs subject by subject  For each case, select one or more controls with characteristics that match that case  Example  Case is a 50 year old African American man, and we want to match on age, race, and gender  Control would be selected who is 50 years old, African American, and male

33. Group Matching  Also called “frequency matching”  For a stratum of cases, select a stratum of controls. The proportion of a characteristic should be the same between cases and controls  Often requires that all cases are selected first  Example  There are 400 cases (300 female, 100 male)  We would select 300 female and 100 male controls if we wanted to match on gender

34. Matching – Positives and Negatives  Positives  Leads to more efficient stratified analyses  Negatives  Cannot examine the relation of a matched variable to the disease  May be increase complexity of study logistics (hard to find a control for some cases)  In individual matching, cannot use cases for which no matched control was found  Risk of “overmatching”, which can result in loss of precision

35. Matching – The Verdict  Be careful when opting for a matched design  Match (if at all) on only a few variables suspected to be strong confounders

36. Module Outline  Case-Control Studies  What are they?  Case Selection  Control Selection  Matching  Potential Biases  Strengths and Weaknesses

37. Potential Biases  Selection bias  Information bias  Recall bias  Interviewer bias  Confounding bias

38. Selection Bias  Control-selection bias  If exposure in selected controls differs from exposure in source cohort  Case-selection bias  If exposure in selected cases differs from exposure in source cohort  If some cases did not arise from the source cohort  Want well-defined inclusion/exclusion criteria and sound selection methods

39. Information Bias  Recall bias  Interviewer bias

40. Recall Bias  Remember that we identify cases and controls based on disease status and then need to determine past exposure  May not be a problem for some exposures (e.g., presence of a gene) but other exposure data rely on interviews or surveys  Recall is a major problem in case-control studies

41. Recall Bias  Some participants may not be able to remember or accurately report information related to exposure  Or they simply may not have the requested information  This means that some cases/controls will likely be misclassified as exposed/unexposed

42. Interviewer Bias  If using interviewers to collect data, they may not be blinded to the case-control status of participants  Interviews may phrase items differently or probe further on exposure questions when interviewing cases

43. Minimizing Information Bias  Exposure status (and other variables) should be measured in a comparable fashion in cases and controls  Exposure status should not be known when a cases or control is selected for study  Sources of exposure information  Self-reports  Surrogate / proxy (e.g., spouse)  Records (hospital, worksite)  Physical measurements  Stored samples

44. Confounding Bias  Confounding: A situation in which the effect or association between an exposure and outcome is distorted by the presence of another variable  If confounding is present in the source cohort, then it should also be present in the study sample  Since we select cases and controls to be “representative” of the source cohort  Several ways to control for confounding  Stratification, statistical modeling, etc.

45. Module Outline  Case-Control Studies  What are they?  Case Selection  Control Selection  Matching  Potential Biases  Strengths and Weaknesses

46. Strengths of Case-Control Studies  Easier to study rare diseases  Can examine a variety of exposures for a given disease  Compared to cohort studies, usually:  Quicker  Easier  Cheaper  Under certain conditions, results can estimate a causal parameter

47. Weaknesses of Case-Control Studies  Difficulty in selecting appropriate controls  Information bias (particularly recall bias)  Not ideal for rare exposures (cohort studies are probably better for this)  Can be difficult to establish temporality between exposure and disease

48. Case-Control vs. Cohort Case-ControlCohort (Prospective) Study Group Diseased persons (cases)Exposed persons Comparison Group Nondiseased (controls)Unexposed persons Multiple Associations Several exposures with disease Several diseases with exposure Cost of Study Relatively inexpensiveExpensive Time Required Relatively shortGenerally long Best When Disease is rareExposure is rare Problems Selection of controls, information bias, etc. Loss to follow-up, misclassify outcomes, etc.

49. Summary “A case-control study is a useful first step when searching for a cause of an adverse health outcome.” Gordis

50. Evidence Pyramid

51. Summary  There are several important strengths to case-control studies, but must be aware of some of the limitations  Biases discussed earlier  Control selection is crucial to a case-control study  Source of controls  Matching

53. Thank you for completing this module If you have any questions, write to me. Paul.Reiter@osumc.edu

54. References  Gordis L. (2009). Epidemiology, 4 th edition. Philadelphia, PA: Elsevier/Saunders.  Rothman, K.J., Greenland, S. & Lash, T.L. (2008). Modern Epidemiology, 3 rd Edition. Philadelphia, PA: Lippincott, Williams & Wilkins.  Rothman, K.J. & Greenland, S. (1998). Modern Epidemiology, 2 nd Edition. Philadelphia, PA: Lippincott, Williams & Wilkins.

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