1. Case-Control Studies

2. Feature of Case-control Studies 1. Directionality Outcome to exposure 2. Timing Retrospective for exposure, but case- ascertainment can be either retrospective or concurrent 3. Sampling Almost always on outcome, with matching of controls to cases

3. Two Characteristics of Cases 1. Representativeness: Ideally, cases are a random sample of all cases of interest in the source population (e.g. from vital data, registry data). More commonly they are a selection of available cases from a medical care facility. (e.g. from hospitals, clinics)

4. 2. Method of Selection Selection may be from incidence or prevalence case:  Incident cases are those derived from ongoing-ascertainment of cases over time.  Prevalent cases are derived from a cross-sectional survey.

5. Characteristics of Controls  Who is the best control? What universe should controls come from?  If cases are a random sample of cases in the population. Then controls should be a random sample of all non-cases in the population sampled at the same time.

6. Three Qualities Needed in Controls  Comparability is more important than representativeness in the selection of controls  The control should be at risk of the disease  The control should resemble the case in all respects except for the presence of disease (and any as yet undiscovered risk factors for disease)

7. Comparability vs. Representativeness Usually, cases in a case-control study are not a random sample of all cases in the population. And if so, the controls must be selected in the same way (and with the same biases) as the cases.

8. If follows from the above, that a pool of potential controls must be defined. This is a universe of people from whom controls may be selected (study base).

9. Study Base Imagining the study base is a useful exercise before deciding on control selection. The study base is composed of a population at risk of exposure over a period of risk of exposure.

10. Cases emerge within a study base. Controls should emerge from the same study base, except that they are not cases. For example, if cases are selected exclusively from hospitalized patients, controls must also be selected from hospitalized patients.

11.  If cases must have gone through a certain ascertainment process (e.g. screening), controls must have also.  If cases must have reached a certain age before they can become cases, so must controls.  If the exposure of interest is cumulative over time, the controls and cases must each have the same opportunity to be exposed to that exposure.

12. Five issues in Matching 1. Control selection is usually through matching. Matching variables (e.g. age), and matching criteria (e.g. within the same 5 year age group) must be set up in advance.

13. Five issues in Matching 2. Controls can be individually matched (most common) or Frequency matched. Individual matching: search for one (or more) controls who have the required matching criteria, paired (triplet) matching is when there is one (two) control (s) individually matched to each cases. Frequency matching: select a population of controls such that the overall characteristics of the case, e.g. if 15% cases are under age 20, 15% of the controls are also

14. Five issues in Matching 3. Avoid over-matching, match only on factors KNOWN to be cause of the disease. 4. Obtain POWER by matching MORE THAN ONE CONTROL per case. In general, N of controls should be ≤ 4, because there is no further gain of power above that. 5. Obtain Generalizability by matching by matching more than one type of control.

15. Advantages and Disadvantages of C-C Studies Advantages: 1. Only realistic study design for uncovering etiology in rare diseases 2. Important in understanding new diseases 3. Commonly used in outbreaks investigation 4. Useful if inducing period is long 5. Relatively inexpensive

16. Disadvantages 1. Susceptible to bias if not carefully designed 2. Especially susceptible to exposure misclassification 3. Especially susceptible to recall bias 4. Restricted to single outcome 5. Incidence rates not usually calculate 6. Cannot assess effects of matching variables

17. Examples of Problems  Doll ’ s 1952 study of smoking and lung cancer. The problem was that the control population ( lung disease) was biased in relation to the exposure.  McMahon ’ s 1981 study of coffee and pancreatic cancer. Problem was that some of the controls may have been biased in relation to the exposure, because diseases related to coffee were excluded from the control series.

18. Some Important Discoveries 1950 ‘ s  Cigarette smoking and lung cancer 1970 ’ s  Diethyl stilbestrol and vaginal adenocarcinoma  Post-menopausal estrogens and endometrial cancer

19. 1980 ’ s  Aspirin and Reyes sydrome  Tampon use and toxic shocks syndrome  L-tryptopham and eosinophilia-myalgia syndrome  AIDS and sexual practices 1990 ’ s  Vaccine effectiveness  Diet and cancer

20. Basic Analysis For one control Data is expressed in a four-fold table, and an odds ratio is calculated (relative risks have no meaning here- why?) CaseControls Exposedab Unexposedcd OR= ad/bc

21. Paired Analysis Case ExposedUnexposed ExposedBothMixed Controls UnexposedMixedNeither

22. Paired Analysis For one control Case ExposedUnexposed Exposed rs Controls Unexposed tu McNemar chi 2 =(t+s) 2 /(t-s)

23. More points about case-control analysis  The odds ratio is a good estimate of the relative risk when the disease is rare (prevalence <20%)  Can be extended to N>1 controls  Statistical testing is by simple chi-square (unmatched analysis) or by McNemar ’ s chi square (matched-pairs analysis)  Can be extended to multiple strata ( Mantel- Haenzel chi-square)

24. Theoretical Foundation  “ Case-control studies should be viewed as efficient sampling schemes of the disease experience of the underlying open or closed cohorts ”  “ The exposure odds ratio derived from case-control studies equals the disease odds ratio derived from cohort studies ”