Case-Control Studies

1. Case-Control Studies Pradeep Deshmukh Professor Dr Sushila Nayar School of Public Health MGIMS, Sewagram

3. Definition…. • The case-control study is an analytic epidemiologic research design in which the study population consists of groups who either have (cases) or do not have a particular health problem or outcome (controls). • The investigator looks back in time to measure exposure of the study subjects. The exposure is then compared among cases and controls to determine if the exposure could account for the health condition of the cases.

4. SYNONYMS • Case-Referent • Case-Compeer • Retrospective

5. Characteristics • Observational / Non-experimental • Occasionally Exploratory • Explanatory (Analytical) • Retrospective • Effect to Cause • Both Exposure & Disease have already occurred • Uses Comparison Group

6. Why case-control design for study of rare diseases? • Consider some rare disease say some cancer (leukemia) • Crude Annual Incidence = 3.4/100000 (< 15 years) • Cohort Study: A year of observation on a million children to identify 34 cases • Sample of 34 cases: Sub-divided in 2 or more exposure categories • What about conducting case-control design?

7. Case-Control Studies for Diseases having long induction period • Advantageous: Long induction period between the exposure and clinical onset of disease • Cohort Study: Waiting years for accrual of cases • Case-Control Study: Compress time • Case-Control Studies: Chronic Diseases (Cancer / Cardiovascular Diseases)

8. Case-control studies in hierarchy of designs • RCT: Methodological Standard of Excellence However, • Ca-Co - Not only SIMPLE to perform but some times the ONLY approach to solve a problem…. • Philosophically no design is ‘Gold Standard’…. • Understand strengths and weaknesses …. • Select appropriate study design to address your RQ……...

9. Progression of study design: Clinical research • Isolated Case Reports • Case Series • Cross-Sectional study • Case-Control Study • Cohort Study • Randomized Clinical Trial • Meta-Analysis

10. PROGRESSION OF STUDY DESIGN: COMMUNITY RESEARCH • Ecological Study • Cross-Sectional Study • Case-Control Study • Cohort Study • Randomized Community Trial • Meta-Analysis • EXAMPLE: • Lipid - Atherosclerosis Association

11. Lipid - Atherosclerosis Association • Analysis of Death Rates from CAD according to per capita fat consumption in 20 countries Hypothesis of L-A association. • CS Studies: Framingham and Evans County Heart Studies (Dawber et al 1971, Cassel 1971) • Case-Control Studies confirmed Association. • Cohort Studies (Truett et al 1967, Tyroler et al 1971) • Community Based Controlled Trials of Lipid Reduction (Lipid Research Clinics Program)

12. Causation/causal association • Criteria to be fulfilled: • Temporal association • Strength of association & effect of cessation • Specificity of association • Consistency of association • Biological plausibility • Coherence of association

13. The setting of case-control research • Clinical • Mechanisms of Disease Causation • Community • Population Health Impact of Exposure

14. Decision to conduct case-control research • The characteristics of the exposure and disease • The current state of knowledge: Relationship • The immediate goals of the study • The research setting • The resources available

15. Research questions • Is OC use associated with MI in women? • Is current IUD use associated with PID? • Is OC use associated with the risk of breast cancer? • Is age at first coitus associated with cervical cancer? • Is legal abortion associated with placenta previa in a later pregnancy?

16. WHO ARE CASES? • With a Specific Outcome: • Presence of Disease / Syndrome • Complications / progression of Disease (Severe dehydration crisis) • Death (Neonatal mortality) • Serum cholesterol / Birth weight • Delayed Immunization • Early Initiation of Cigarette Smoking • Adverse Reactions of Drugs / Vaccines (SIDS) • Behavior (Juvenile Delinquency) • Drug Resistance (MDR-TB) • Couple as a case (Infertility)

17. Selection of cases (Definition) • Diagnostic Criteria • Risk of Disease Misclassification • Continuous / Discrete Outcome Variable • Relatively simple & straightforward: Children with cleft palates (physical examination) • Sometimes difficult: Hypertension • Diagnosis: Combination of methods • Rationale / Logical • Criteria Specific • Operational versus Rigid • Standard Definition (WHO, CDC, etc) • Reference (growth references – NCHS, CDC, New WHO)

18. Selection of cases (definition) • Eligibility Criteria • Inclusion/Exclusion criteria • Ca-Co studies should be limited to incident cases (Sackett 1979): • Exposures are presumably more recent and therefore more reliably recalled. • Relatively homogeneous group • Exclusion of prevalent cases: Minimize the Selection Bias (Neyman Fallacy). • Ex: PID and IUD Use • Women who are not sexually active or who have had a tubal ligation are not likely to have recently used any contraceptive method including IUDs

19. Case definition • Conceptual definition • Obesity defined as body fat percentage > 33% • Operational definition • Body Mass Index > 30

20. Case definition: Issues • Case definition should avoid misclassification • For example: Sinha et al (2008) • Anemia was defined as Hemoglobin < 110 gm/L as measured by WHO Colour Scale • WHO Colour Scale over-estimates the hemoglobin • Misclassified cases with mild anemia • Also, studying mild forms of cases, gives larger case group; but misclassifies cases as non-cases OR non-cases as cases as early diagnosis is generally imprecise

21. Case definition: Issues • A severe case definition may exclude people who have been cured or who died of disease before the condition was severe enough to be labelled as case • Standard/consensus definitions if available, must be used • For example, • Rheumatoid arthritis – Rome criteria, NY criteria, 1987 ARC criteria • Metabolic Syndrome – ATP III, IDRF, and so on • Lack of agreement over definition may introduce variability in estimates of effect

22. Case definition: Issues • The issues of severity, diagnostic criteria and subjectivity of criteria all lead to potential problems of misclassification of cases • The researcher can choose between more restrictive and inclusive definitions • Think in terms of sensitivity and specificity of definition and its effect on validity, sample size, precision and power • Brenner and Savitz (1990) reported that • Restrictive definition (less sensitive) leads to lack of precision and power by reducing sample size • Broad criteria (less specificity) produce misclassification leading to biased measure of effect • So, weigh validity - specificity over sensitivity (Restrictive definition over inclusive definition)

23. Sources of cases (Research Setting): • Hospitals (Multi-Centric Studies) • Community • Industrial Population

24. Identification of cases: Issues • The goal is to • Ensure that all true cases have an equal probability of entering the study and that no false cases enter • Example: Conceptual definition of HIV • Factors affecting decision to test/access the test and Sn & Sp of test will decide who eventually becomes a case under operational definition • Selection bias ??

25. Biases • Selection bias • Unequal chance of getting into study • Berkson’s bias • Variable rate of hospitalization affecting case selection • Neyman fallacy • Incident case Vs prevalent case • Detection bias • Due to closer medical attention, detection of endometrial cancer was more in a group using estrogen

26. Selection of Controls • The controls should come from the population at risk of the disease • Men can not be controls for a gynecological condition • The controls should be “eligible for the exposure” • The controls should have same exposure rate as that of the population from where the cases are drawn

27. Wachlder’s four principles for selection of controls • The study base • Source of case and the control should be the same • Deconfounding • Comparable accuracy • Similar misclassification errors in cases & controls • Same potential of recall bias in cases & control • Efficiency

28. Types of Controls • Hospital or clinic control • Dead control • Controls with similar diseases • Peer or case-nominated (friend/neighbor) control • Population controls

29. Hospital controls • Readily available hence commonly used • Main reasons to use hospital controls are • To select controls whose referral pattern is similar to cases • To obtain similar quality of examination • For convenience • May not be representative of the population

30. Dead controls • Might use dead controls for dead cases • In some situations, this might lead to use of surrogate informant • The problem is the dead control is not representative of the living population • McLaughlin compared dead controls with living controls and noticed that the dead controls smoked more cigarettes and consumed more alcohol than living controls • Appropriateness depends on the exposure being studied

31. Controls with similar diseases • Reasons • To minimize the recall bias • To minimize the interviewer bias • To examine the specificity of an exposure for a particular type of cancer • For “practical” but unspecified reasons • Problem ??

32. Peer or case-nominated (friend/neighbor) control • Neighborhood controls is used in two ways: • To refer to community or population controls • To refer to controls selected from finite number of close neighbors • Search starts from house of the case and door-to-door search conducted for eligible controls in a standardized pattern • Friend or neighbor control is a surrogate for matching on age, SES, education, etc • A quick way to find control • Bias is introduced if determinants of friendship are associated with disease or exposure • Friends share many risk behaviors

33. Population controls • Randomly drawn from population • Truly representative of population • Ideal way of selecting controls • Practically, very difficult to carry out • Study base ???

34. Where to select controls from? • Way the pros and cons • Analyze the situation for bias being introduced • If possible, • select different sources of controls and compare with each other • Compare the inferences drawn

35. Ratio of control to cases • Statistical consideration • When the number of subjects available in one group (cases) is limited, an increase in the other group increases the study power • Gain in power is till the ratio of 4:1 • Thereafter, the gain is not substantial but cost increases • When the study of power with equal allocation is as high as 0.9 or as low as 0.1, additional fails to increase the power

36. Ratio of control to cases • Validity of inferences • Even when there is no statistical need, more than one control may be recruited per case • Enrolling two or more types of controls is a way of checking for biases introduced by choice of control group • If the measure of effect is similar when comparing cases with each control group • Probably – no biases (no surety) • If different measure of effect, then the bias is there and the researcher can understand it

37. MATCHING • Purpose: To adjust - effects of relevant confounders • Matching in Design - Accounted in Analysis • Misconception: The goal is to make the case and control groups similar in all respects, except for disease status. • An Optimal Matching Scheme involves only those variables which improve statistical efficiency or eliminate bias from the effect of interest.

38. MATCHING • Which variables are appropriate for matching? • Risk factors from prior work may be identified for matching • Matching by interviewer or hospital may be used to balance out the effects of interviewer and observer errors • It is best to limit matching to basic descriptors (age, race, sex, etc) • Non-modifiable risk factors • Use few matching factors

39. MATCHING Overzealous matching may have adverse effects: • Matching on a strong correlate of the exposure, which is not an independent risk factor for the outcome (overmatching) may lead to an underestimate of OR. • Matching may lead to a false sense of security that a particular variable is adequately controlled.

40. Sample size • Epi_info 6.04

41. Measurement of Exposure • Questionnaires • Records • Conversion tables/algorithms

42. Measurement of exposure • Questionnaire • Question comprehension • Information retrieval • Response formulation and recording • Quality of exposure reports may be influenced by • Type of respondent • Administration of questionnaire • Salience of exposure • Way in which information is retrieved • Ways in which responses are formulated and recorded

43. Measurement of exposure • Records • Abstraction of data from record • Quality control measures are important • Careful design and testing of abstraction form • Training and supervision of abstractors • Priori definition of terms • Specifications of rules for handling conflicting or missing data

44. Measurement of exposure • Conversion tables/algorithm • To obtain more specific exposure measure from questionnaire or record • More in use now-a-days for dietary and occupational variables

45. Group work • Three groups • Design a case-control study

46. Analysis

47. Associations • Use of tests of significance • Estimation of Odds ratio and its confidence interval • Attributable risk estimation

48. Tests of significance • Unmatched study • Matched study

49. Binary exposure without covariates • OR = ad/bc • SE(OR) = eSqrt (1/a+1/b+1/c+1/d) • CI = OR exp (± Z 1-a/2eSqrt (1/a+1/b+1/c+1/d))

50. Binary exposure and categorical covariate • Stratified analysis • Calculate OR for each strata • Mantel-Haenszel summary odds ratio = å aidi/ni = ------------------- å bici/ni • Logistic regression