1. A short introduction to epidemiology Chapter 2b: Conducting a case- control study Neil Pearce Centre for Public Health Research Massey University Wellington, New Zealand

2. Chapter 2 (additional material) Case-control studies This presentation includes additional material on conducting a case-control study More information on data analysis is given in chapter 9

3. Chapter 2 (additional material) Case-control studies Reasons for doing a case-control study Basic study design Selection of cases Selection of controls –control sampling strategies –sources of controls –issues in control selection

4. Birth End of Follow up Death other death lost to follow up “non-diseased” symptoms severe disease

5. A Hypothetical Incidence Study

6. A Hypothetical Case-Control Study 1813/8187a/c ad Odds ratio = ----------------= -----= ---- 952/9048b/d bc 1813/952a/b ad =----------------=-----= ---- 8187/9048c/d bc

7. Reasons for Doing a Case-Control Study It may be inefficient to have to obtain exposure information on all people in the source population It is sufficient to obtain information on all of the 2,765 deaths and a control sample (e.g. 2,765 controls) of the 17,235 survivors We therefore only need to get exposure information on 5,530 people instead of 20,000 This gain in efficiency is much greater when the disease is “rare” (e.g. if it were 1/10 th as “common” then we would have 277 cases and 277 controls)

8. Reasons for Doing a Case-Control Study Rare disease long induction time smaller study size permits collection and analysis of more detailed exposure information cohort difficult to enumerate (registry-based studies)

9. Chapter 2 (additional material) Case-control studies Reasons for doing a case-control study Basic study design Selection of cases Selection of controls –control sampling strategies –sources of controls –issues in control selection

10. Basic Case-Control Study Design Every study is based on a particular source population followed over a particular period of time (the risk period) Ideally the study base should be made explicit We study all cases of the outcome and a sample of controls drawn from the source population The case-control design thus involves all of the potential biases involved in a full cohort study, as well as additional biases involved in sampling controls Information bias is not an inherent feature of such studies

11. Cohort-Based (Nested) Case-Control Studies Enumerate the cohort (source population) and its experience over time (the risk period) Ascertain all cases generated by this study base Sample controls from the person-time (or persons) that generated the cases

12. Registry-Based Case-Control Studies Ascertain all cases appearing in the registry during a specified period of time Sample controls from the source population for the registry

13. Chapter 2 (additional material) Case-control studies Reasons for doing a case-control study Basic study design Selection of cases Selection of controls –control sampling strategies –sources of controls –issues in control selection

14. Selection of Cases Cohort-based All cases (or deceased cases) generated by the cohort study Living cases may be added from other sources (e.g. hospital records, cancer registrations) Registry-based All eligible cases appearing in the “registry” during a specified period of time

15. Chapter 2 (additional material) Case-control studies Reasons for doing a case-control study Basic study design Selection of cases Selection of controls –control sampling strategies –sources of controls –issues in control selection

16. Control Sampling Strategies Cumulative incidence sampling Case-base sampling Density sampling

17. Birth End of Follow up Death other death lost to follow up “non-diseased” symptoms severe disease

18. A Hypothetical Incidence Study

19. Cumulative Incidence Sampling “Traditional” method of control selection in nested case-control studies Controls are sampled from the “non-cases”, those free of disease at the end of the follow- up period, i.e. the survivors I.e. controls are sampled from the denominators for (cohort) odds ratio analyses

20. A Hypothetical Incidence Study

21. A Hypothetical Case-control Study

22. Cumulative Incidence Sampling Estimates the (cohort) odds ratio (without any rare disease assumption) Estimates the risk ratio and rate ratio approximately (with a rare disease assumption) May involve matching on age, etc Exposure is usually only considered up to the “time” (year or age) that the case occurred

23. Case-cohort Sampling Controls can be selected from those at risk at the beginning of the follow-up period, I.e. from the entire source population I.e. controls are selected from the denominators for (cohort) risk ratio analyses

24. A Hypothetical Incidence Study

25. A Hypothetical Case-control Study

26. Case-cohort Sampling Estimates the risk ratio (without any rare disease assumption) Requires minor modifications to the standard formulas for confidence intervals and p-values May involve matching on age, etc Once again, exposure is usually only considered up until the “time” that the case occurred

27. Birth End of Follow up Death other death lost to follow up “non-diseased” symptoms severe disease

28. Density Sampling Controls are selected longitudinally throughout the course of the study, i.e. from the person-time of the study base I.e. controls are sampled from the denominators for the rate ratio analyses In general, controls are selected from the “risk set” of persons at risk at the “time” that each case occurred

29. A Hypothetical Incidence Study

30. A Hypothetical Case-control Study

31. Density Sampling The “time” variable is usually taken to be age rather than calendar time (year) Estimates the rate ratio (without any rare disease assumption) Matching may also be done on other time- related factors, although this is usually not necessary Usual method of sampling in registry- based studies

32. Selecting Controls Cohort-based studies Sample of the cohort (preferably by density sampling on age) Registry-based studies Sample of the source population for the Registry (usually by density sampling on year, perhaps with matching on age)

33. Selecting Controls in Registry-Based Studies Cases chosen from all lung cancer cases at hospitals in the City Controls chosen from general population of the City?

34. Selecting Controls in Registry-Based Studies All lung cancer cases at all hospitals in the City Controls chosen from general population of the City? Restrict cases to those living in the City (exclude those who have come to the City for treatment) Restrictions that apply to one group (e.g. having a telephone, being on Electoral Roll, having health insurance) should also be applied to the other

35. Selecting Controls in Registry-Based Studies Cases chosen from all lung cancer cases at the main hospital in the City What is the source population for these cases?

36. Selecting Controls in Registry-Based Studies Cases chosen from all lung cancer cases at the main hospital in the City What is the source population for these cases? “All those who would have come to the main hospital in the City for treatment if they had developed lung cancer”

37. Issues in Control Selection Controls are usually sampled at random from the entire study base However, it is sometimes desirable to restrict the controls to a sample of a subset of the study base In particular, we may select controls from persons with other diseases generated by the same study base (e.g. other deaths, other cancers, other hospital admissions)

38. “Other Disease” Controls All other diseases All other diseases except those known to be related to exposure A disease “known to be unrelated to exposure”

39. Reasons for Using “Other Disease” Controls The cohort (source population) is not enumerated E.g. if the cases are identified from hospital admissions (e.g. for lung cancer) then the study base is “all persons who would have been admitted to this hospital if they had developed lung cancer” Controls might be selected from other admissions to the same hospital

40. Reasons for Using “Other Disease” Controls Comparability of information E.g. in a case-control study of non-Hodgkin’s lymphoma and pesticide exposure, cases might be more likely to recall brief exposures We might therefore select controls from “other cancer” registrations rather than from the entire source population for the Cancer Registry

41. Selection Bias in Case-Control Studies In a case-control study, the controls are a sample of the source population Selection bias can occur if the sample is non- random, and the selection of controls is related to exposure status In other words, selection bias can occur if the controls are not representative of the exposure in the source population

42. Selection Bias in Case-Control Studies: Solutions Selection bias can occur if the selection of controls is related to exposure status In the analysis, we can control for the determinants of control selection (e.g. social class) An exception is when we have chosen “other disease” controls and the other diseases are directly caused by the main exposure of interest: this selection bias cannot be removed

43. General Population and “Other Cancer” Controls General population Represents study base May be more prone to recall bias if cases are more likely to recall exposures Difficult to keep interviewer blind, and may get interviewer bias “Other cancers” Other diseases may be caused by exposure (selection bias) Equal motivation and recall in cases and controls Easier to keep interviewer blind

44. Reasons for Matching Practical efficiency e.g. if we are using hospital controls then it is usually more efficient to select a control admitted on the same day as the case, rather than sampling at random from all admissions for the year

45. Reasons for Matching Statistical efficiency e.g. if we select general population controls at random in a lung cancer case-control study then the cases will be mostly “old” and the controls will be mostly “young”. It will therefore be difficult to stratify on, and control for, age

46. Reasons for Not Matching Practical efficiency matching can be costly and time-consuming and is usually not necessary since we can adjust for the major matching factors (e.g. age, gender, smoking status) in the analysis

47. Reasons for Not Matching Statistical efficiency Matching on a weak risk factor (or a non-risk factor) that is strongly correlated with the main exposure can dramatically reduce efficiency

48. Matching Only match on risk factors that are: Not of intrinsic interest in themselves (e.g. age) Strong risk factors for disease Not too difficult to match on

49. Common misconceptions about case- control studies Fundamentally different type of study design that proceeds from disease to exposure (I.e. reverse causality) Inherently less valid (more biased) than cohort studies Require a rare-disease assumption Odds ratio only approximates the rate ratio or risk ratio (under the rare disease assumption)

50. A short introduction to epidemiology Chapter 2b: Conducting a case- control study Neil Pearce Centre for Public Health Research Massey University Wellington, New Zealand