1. Case Control Studies Önder Ergönül, MD, MPH Koç University, School of Medicine Summer Course on Research Methodology in Health Sciences Julne 16, 2015, Istanbul

2. An Alerting Situation!

3. Objective To identify risk factors for the occurrence of Kaposi's sarcoma and Pneumocystis carinii pneumonia in homosexual men, we conducted a case-control study in New York City, San Francisco, Los Angeles, and Atlanta. Methods Fifty patients (cases) (39 with Kaposi's sarcoma, 8 with pneumocystis pneumonia, and 3 with both) and 120 matched homosexual male controls (from sexually transmitted disease clinics and private medical practices) Results a larger number of male sex partners Cases were also more likely to have been exposed to feces during sex, have had syphilis and non-B hepatitis, have been treated for enteric parasites, and have used various illicit substances. Italians (HLA-DR5) National Case-Control Study of Kaposi’s Sarcoma and Pneumocystis carinii Pneumonia in Homosexual Men Jaffe HWf, et al. Ann Int Med 1983; 99: 145-151.

4. 2008 Nobel Awards Francoise Barre-Sinoussi Luc Montagnier Harald zur Hausen

5. The Cohort Study Purpose: to identify or designate a group of people (cohort) and follow them over time – Exposure is typically measured prior to the onset of disease Cohort Population Disease Assessment Exposure Assessment 1 …… Exposure Assessment N

6. Benefits of Case-Control Studies Cohort Studies are inefficient in the event of: 1.Significant time between exposure and measurable disease outcome 2.Rare outcomes In these cases, cohort studies are often expensive Exposure Assessment Disease Appearance Many, many, many years Initial Cohort N=5,000 Exposure Assessment Diseased Group Identified N=30

7. Case Control Studies

8. Case-Control Studies: Basic Outline 1.Define source population: group that yielded the cases (people with the disease outcome) 2.Determine which cases were/were not exposed to the variable(s) of interest 3.Sample control group from the source population Controls must be sampled independently of exposure 4.Determine which members of the control group were/were not exposed to the variables of interest 5.Compare rates of exposure in the case and control groups

9. 1.Define source population: group that yielded the cases 2.Sample cases 3.Sample control group (from the source population) 4.Determine exposure for all participants 5.Compare rates of exposure in cases vs. controls – Compare using statistics – Odds ratios, chi-square, etc. Steps in Case Control Study

10. 1. Selection of controls 2. Obtaining exposure history Crucial Validity Points

11. Controls provide (an estimate of) the background rate of exposure 1) Controls must be representative of case population 2)Control selection must be independent of exposure Selection of Controls

12. Basic Concept Graphically Source Population = exposed = unexposed Control Group: Should contain representative exposure distribution Cases

13. Example: Is a new blood pressure medication associated with myocardial infarction? – Cases – patients with MI from the cardiology ward of hospital – Controls – patients without MI from the ER ward of the same hospital Problems? – Cardiology ward is referral center for whole state but ER serves mostly the local city – BP medication may be available to local city but not the rest of the rural state Solutions: 1. Choose controls from the whole state OR 2. Exclude all cases who live outside the city Selection of Controls Controls must be representative source population

14. Example: Is a new blood pressure medication associated with myocardial infarction? Problem: – What if the drug slows reaction time, causing automobile accidents that lead to ER visits? – Control sample is not independent of exposure – Bias is introduced – control group has higher proportion of individuals exposed to the new drug Selection of Controls Control selection must be independent of exposure

15. Control Selection In density case-control studies, control group sampled to represent person-time distribution of total exposed and unexposed cohorts ‘ Risk set approach’- select controls from people in the source population who are at risk of becoming a case at the exact time a case is diagnosed As such (in brief): P (selection into control group) should be proportional to person-time contribution e.g. a person at risk for disease for 3 years should have a 3 times higher probability of being selected as a control than a person at risk for 1 year Controls should be eligible to become cases both at the time of selection, and throughout their time in the study Each case in a C-C study should have been eligible to be a control

16. Sources of Controls Population Cases come from clearly defined group Controls sampled from this population Most feasible in population registry cases Neighborhood If group cannot be fully identified (enumerated) Involves sampling homes Often employs ‘matched sample’ design

17. Sources of Controls Hospitals or Clinics Cases are people who received treatment at clinic X when they got the disease Controls are people who would be treated at clinic X if they got the disease May be difficult to identify this group! e.g. How do you define the population who would seek treatment at a regional medical center? How might you define and seek such controls? Telephone Recruiting Limit case eligibility to people who have telephones in a specific area Randomly calling telephones in that area can approximate a random sample of the source population

18. – Use two separate control groups (e.g., hospital and community controls) and if results are the same, supports validity OR – Select 1 large, well-reasoned control group and invest $$ and effort in this group. How many control groups?

19. If costs are similar for cases and controls: – Most efficient: roughly equal numbers If number of cases is small and cannot be increased: – Increasing the number of controls up to a ratio of 4 controls: 1 case improves power of the study – Beyond 4:1 little power increase – Increasing number of controls narrows the confidence interval around the odds ratio, but DOES NOT address validity How many controls per case?

20. Sampling Controls: Density Case- Control Design There are many methods of sampling controls, density-based sampling is one of the most common Comes from the term incidence density (incidence rate) For a given period of time: For the exposed: For the unexposed: I 1 = a / PT 1 I 0 = b / PT 0 Where I 1, a, and PT 1 are the incidence rate, number of cases, and total person time accumulated in the exposed group (symbols are equivalent for the unexposed group)

21. Sampling Controls: Density Case- Control Design Goal of density-based sampling: estimate the contribution of the exposed and unexposed cohorts in the source population to total person time – The odds ratio should estimate the incidence rate ratio Where: c = number of exposed people in control sample d= number of unexposed people in control sample

22. Sampling Controls: Density Case- Control Design Accordingly Fundamental assumption: As such, accurately identifying the source population and taking a random sample is essential

23. Exercise 1: Analyzing Density-Based Case-Control Data Exposure: Severe sun burn Yes (%)No (%)Total A. Skin cancer cases87 (75.0)29 (25.0)116 B. Cohort findings (person-years)53,697 (59.6)36,348 (40.4)90,045 C. Control group (people)591 (59.1)409 (40.9)1,000 D. Rate (cases/10,000 person years)16.28.012.9 |OR – IRR| = 0.05 Increasing sample size reduces standard error. Standard error estimates the difference between a sample statistic (e.g. proportion of population exposed) and a true value (actual proportion exposed). The quality of a C-C estimate depends on the quality of the control sample!

24. Odds ratio significance test, large sample approximation method How do we interpret this? Can we say that these data suggest severe sun burn is a risk factor for skin cancer?

25. 25 Case Control Study

26. Recall bias: cases remember exposures more often than controls Example: study of oral contraceptive use and breast cancer – Women with breast cancer have greater reason to search their memories for exposures that might be associated with their disease – The cases are more likely to remember oral contraceptive use than controls – Bias – over-estimates the risk Measurement of Exposure

27. Interviewer bias: Data gatherers should be blind to case status – If not possible, keep interviewers blind to main hypothesis Problem: If interviewers are not blind, they may (inadvertently) elicit information differently. – Spend more time data gathering – Ask more follow-up questions Measurement of exposure

28. Memory aids (Photographs, diaries, timelines etc.) Example: study of oral contraceptive use – photographs of pills with names – timeline to recall major life events. Measurement of exposure; solutions

29. Nested Case-Control Studies The term typically refers to a CC nested within a clearly defined cohort (e.g. the Nurses’ Health Study or the ELSPAC groups) Rationale: – You may want more data (specific to your outcome) than is available – Too expensive to get new information from everyone in the cohort – Instead, sample controls randomly from the cohort and only collect relevant data from them A good (and common) opportunity to employed matched CC designs

30. Crossover studies: an experiment in which two interventions are compared; each subject acts as their own control – Each subject receives both interventions The effects of each intervention are compared for every subject There needs to be enough time between interventions to see effects The Case-Crossover Design Intervention 1 Measure effect 1 Intervention 2 Measure effect 2

31. Case-Crossover Design: all subjects are cases Controls are not different people- they are a sample of the cases’ time before disease Useful when considering short-term ‘trigger effects’ – e.g. coffee and asthma attack – H 0 = coffee elevates risk of asthma attack for 1 hr – ‘Case time’- one hour periods after drinking coffee – ‘Control time’- a sample of 1 hour periods not after drinking coffee – Calculate IRR The Case-Crossover Design

32. Matched Case-Control Studies Matching is employed because: – It addresses confounding in the design phase of a study – It improves the efficiency of the analysis Example of confounding What happens if cases are older than controls? Bias Cigarette smoking Heart Attack Age

33. Matched Case-Control Studies Matched analyses are more difficult If matching variable are ‘important’ (related to your variable of interest), they should be included in your analysis – If matching is used simply to structure sampling (e.g. the neighborhood recruitment example), and the matching variable are not important, they do not have to be included

34. Multivariable Models and Case-Control Studies Odds ratios can stand alone only in the absence of confounding and modifying variables: very rare! The multivariate extension of a non-matched case control study is logistic regression Odds (log odds) associated with sunburn Controlling for potential confounder Allowing effect of sunburn to vary between people with different skin tones

35. Design Comparison: Cohort and Case-Control Studies Cohort StudyCase-Control Study Complete source populationSampling from source population Very expensiveLess expensive Convenient for studying many diseasesConvenient for studying many exposures Can calculate incidence rates/risks and their differences/ratios Can usually only calculate the ratio of rates or risks Can be retrospective or prospective

36. Advantages of Cohort Studies cohortcase-control Complete exposure information; Less bias for exposure Recall and selection bias Can examine temporal relationNot always Study multiple outcomesOnly one outcome Incidence rates and RROR Results easy to understand, straightforward More difficult interpretation

37. Disadvantages of Cohort Studies cohortcase-control Inefficient for rare disease, unless the attributable risk percent is high Optimal for rare disease If prospective, extremely expensive and time consuming Cheaper and quick If retrospective, requires the availability of the records Validity of the results can be seriously affected by losses to follow-up