1. Leicester Warwick Medical School Health and Disease in Populations Case-Control Studies Paul Burton

2. Lecture Objectives You should be able to: 1.Describe the principles underlying case-control studies 2.Describe the differences and similarities between case-control studies and other epidemiological designs 3.Outline the factors which suggest that a case- control design might be suitable for a particular epidemiological question

3. Lecture Objectives 4.Describe the limitations and assumptions inherent to case-control designs 5.Estimate the strength of an association from the result of a simple case-control study, and calculate and interpret the error factor and 95% confidence interval for this estimate Recommended reading from Prescribed book: Farmer and Miller, Ch 6, pp56-67

4. A hierarchy of study designs

5. Cohort Studies Exposed Unexposed Time Count events and pyrs

6. Cohort and case-control studies Cohort study (bladder cancer and cigarette smoking) Exposed: 100 cases in 100,000 people followed over 10 years = 1,000,000 pyrs Unexposed: 10 cases in 200,000 people followed over 10 years = 2,000,000 pyrs IRR = (100/1,000,000)  (10/2,000,000) = 20

7. Cohort and case-control studies Now ask question the other way around What are the odds of having been a smoker if you are a case? 100:10 = 100/10 = 10 What are the odds of having been a smoker if you are not a case? 99,900:199,990 = 99,900/199,990 = 0.4995 What is the ratio of the odds (OR=odds ratio)? 10/0.4995 = 20.02 NB (100/99,900)  (10/199,900) = (100/10)  (99,900/199,900) = 20.02 Very similar result to cohort analysis

8. Cohort and case-control studies What if we only have a 10% sample of the non-cases? What are the odds of being a smoker if you are a case? 100:10 = 100/10 = 10 What are the odds of being a smoker if you are not a case? 9,990:19,999 = 9,990/19,999 = 0.4995 What is the ratio of the odds (OR=odds ratio)? OR=10/0.4995 = 20.02 Exactly the same result!!

9. Cohort and case-control studies What if we only have a 50% sample of the cases and a 20% sample of the non-cases?: What are the odds of being a smoker if you are a case? 50:5 = 50/5 = 10 What are the odds of being a smoker if you are not a case? 19,980:39,998 = 19,980/39,998 = 0.4995 What is the ratio of the odds (OR=odds ratio)? OR=10/0.4995 = 20.02 Exactly the same result again!!

10. The general case ( full population data)

11. Sampling fractions: 0.637 in cases, 0.02 in non-cases

12. Sampling fractions Regardless what proportion of all possible cases are collected (the sampling fraction in cases) and what proportion of all possible non-cases (the sampling fraction in non-cases) the two sampling fractions always cancel in calculating the odds ratio (OR) If we now call the non-cases “controls” this is a “case-control study”

13. Case-control studies We compare the odds of having been exposed in cases with the odds of having been exposed in the controls This gives us an odds ratio (OR) which is unaffected by the potentially different sampling fractions in cases and controls

14. Case-control Studies Case Non-Case (Control) Exposed? Time

15. Conducting a case-control study Identify a group of cases Identify a suitable group of non-cases Ascertain exposure status of everyone Compare level of exposure in cases and controls

16. Why use a case-control approach? Quick Fundamentally retrospective: no need to wait for a follow-up period

17. Why use a case-control approach? Cheap With a rare disease, most people in a cohort study will not develop disease and so most of the follow-up will be of people who contribute little information By using a low sampling fraction in controls in a case-control study you avoid having to collect information on a large number of non-cases

18. Expected yield of cohort studies:

19. The OR and the IRR Original example: IRR=20, OR=20.02 “The rare disease assumption” The approximation gets better and better as a disease gets rarer and rarer in the general population There is a special form of case-control study based on what is called “incidence density sampling” for which the approximation is always perfect – you don’t need to know about this for the HaDPop course Even when the IRR and OR are different ( e.g. IRR=5.1, OR=6.3) both are still measures of some sort of ‘risk ratio’ and both are therefore useful. It is not that one is ‘right’ and one is ‘wrong’: they express the same thing in a slightly different way.

20. Benefits of case-control studies Good for rare outcomes Possible to look at a lot of different exposures in detail Often no practicable alternative

21. Limitations of case-control studies No estimate of population incidence, only of relative risk The differing sampling fractions always cancel out in calculating ad/bc, but not in trying to calculating e.g. c/d (the odds of someone in the general population being a case if they are unexposed). Unless you know the sampling fractions More prone to bias: Information bias Selection bias It can be impossible to determine whether the disease causes the exposure or vice versa Not suitable for rare exposures

22. Information bias Does cigarette smoking cause ischaemic heart disease? Cases: average 5 cigarettes/day Controls: average 5 cigarettes/day Looks as if the exposure is not associated with the disease. But: True exposure in cases: 10 / day True exposure in controls: 5 / day Here, cases tend to understate their intake In addition Random errors push OR towards 1.0 (shrinkage)

23. Selection bias Case-control study of lung cancer and smoking Get cases of lung cancer from the respiratory medicine wards. Get controls as a random sample of patients from the same wards who do not have lung cancer But, smoking causes lots of other respiratory diseases as well as lung cancer so the patients on the ward are not a representative sample of the general population. Will underestimate OR.

24. Analysis 95%CI: OR  e.f., OR  e.f.

25. How many controls? Unlike an IRR, the precision of an OR is affected by the number of healthy people (x and z): So, it is worth increasing the number of controls - up to a point (typically up to 4-6 times as many controls as there are cases)

26. Creutzfeld Jacob Disease (CJD) and occupation Odds ratio = (9×104)/(3×13) = 24 95% CI: 24÷4.29, 24×4.29 = (5.59, 103.0)

27. Multiple levels of exposure

28. Retrospective v prospective? Confusing terminology: two different issues (1) Does the analysis look forwards or backwards? (2) Are the data collected as and when they occur ( i.e. prospectively) or from historical review - questionnaire, case-notes or other health records – ( i.e. retrospectively). Cohort analysis always looks forwards in time: Given exposure status at baseline, how many events occurred over time in how many person years and what is the incidence rate ratio? Simple case-control analysis is usually expressed as being backwards in time: Given case-control status now, what is the ratio of the odds of exposure at baseline?

29. Retrospective v prospective? Confusing terminology: two different issues (1) Does the analysis look forwards or backwards? (2) Are the data collected as and when they occur ( i.e. prospectively) or from historical review - questionnaire, case-notes or other health records – ( i.e. retrospectively). Conventional cohort study: prospective Historical cohort study: retrospective Conventional case-control study: retrospective

30. Comparison of cohort and case-control studies

31. Rooms for mid-module assessment 9.30-10.30 14 th March 2002 Warwick Students Use their normal small group session rooms Leicester Students MSB room LT1 (candidate numbers 1-63) MSB room 206 (candidate numbers 64-114) MSB room 320 (candidate numbers 115 onwards)