Study designs: Case-control studies

1. Principles of Epidemiology for Public Health (EPID600) Study designs: Case-control studies Victor J. Schoenbach,PhD home page Department of EpidemiologyGillings School of Global Public HealthUniversity of North Carolina at Chapel Hill www.unc.edu/epid600/ Case-control studies

2. From my uncle Are you the weakest link? Below are four (4) questions. You have to answer them instantly. You can't take your time, answer all of them immediately. OK? Let's find out just how clever you really are. Ready? Case-control studies

3. Question 1 You are participating in a race. You overtake the second person. Question: What position are you in? Case-control studies

4. Question 1 – answer If you answer that you are first, then you are absolutely wrong! Answer: If you overtake the second person and you take his place, you are second! Case-control studies

5. On to question 2 Try not to screw up on the next question. To answer the second question, don't take as much time as you took for the first question. Case-control studies

6. Question 2 Question: If you overtake the last person, then you are …? Case-control studies

7. Question 2 – answer Answer: If you answered that you are second to last, then you are wrong again. Tell me, how can you overtake the LAST person?! …? You're not very good at this are you? Case-control studies

8. On to question 3 The third question is very tricky math! Note: This must be done in your head only. Do NOT use paper and pencil or a calculator. Try it. Case-control studies

9. Question 3 – what is the total? Take 1000 and add 40 to it. Now add another 1000. Now add 30. Add another 1000. Now add 20. Now add another 1000. Now add 10. Case-control studies

10. Question 3 – answer Did you get 5,000? The correct answer is actually 4,100. Don't believe it? Check with your calculator! Case-control studies

11. On to question 4 Today is definitely not your day. Maybe you will get the last question right? Case-control studies

12. Question 4 Mary's father has five daughters: 1. Nana, 2. Nene, 3. Nini, 4. Nono. Question: What is the name of the fifth daughter? Case-control studies

13. Question 4 – answer Answer: Nunu? NO! Of course not. Her name is Mary. Read again. (“Mary's father has five daughters.…”) You ARE the WEAKEST LINK!!!!!! Good-bye!!! (With Love, Your Uncle) Case-control studies

14. Plan for this lecture • Confidence intervals and significance tests (read only) • Incidence density and cumulative incidence (brief) • Attributable risk (brief) • Theoretical overview of case-control studies as a complement to the traditional perspective Case-control studies

15. Confidence intervals & significance tests • Everything you’ve been told so far about confidence intervals and statistical significance is probably misleading, including this statement. • I am not licensed to teach statistics, so what I say on this topic mustn’t leave this room! Case-control studies

16. Confidence intervals • “a plausible range of values for the unknown population parameter” Michael Oakes, Statistical inference, p.52 • Exact interpretation is problematic • We are more confident that a 95% interval covers the parameter than a 90% interval, but the 95% interval is wider (provides a less precise estimate) Case-control studies

17. Significance tests “It might be argued that the significance test, if properly understood, does no harm. This is, perhaps, fair comment, but anyone who appreciates the force of the case presented in this chapter will realize that equally, it does very little good.” Michael Oakes, Statistical inference, p.72 Case-control studies

18. Incidence rate and incidence proportion[incidence density and cumulative incidence] Case-control studies

19. IR (ID) and IP in a closed cohort } CI } 1 – CI T0 T1 Case-control studies

20. Attributable risk Case-control studies

21. Attributable risk Assume that we know a causal factor for a disease. Conceptually, the “attributable risk” for that factor is: 1. difference in risk or incidence between exposed and unexposed people or 2. difference in risk or incidence between total population and unexposed people Case-control studies

22. Attributable risk Attributable risk can be presented as: 1. an “absolute” number, e.g., “80,000, or 20 per 100 cases/year of stroke are attributable to smoking” 2. a “relative” number, e.g., “20% of stroke cases are attributable to smoking”. (analogy: a wage increase in a part-time job: $ increase, % increase in wage, % increase in income) Case-control studies

23. For relative measures, think of % of cases Incidence rate or proportions People Case-control studies

24. For relative measures, think of % of cases Caseload Substitute population Case-control studies

25. For relative measures, think of % of cases Caseload Case-control studies

26. Case-control studies Case-control studies

27. Case-control studies • Traditional view: compare - people who get the disease - people who do not get the disease • “Controls” a misnomer, derived from faulty analogy to controls in experiment • Modern conceptualization: controls are a “window” into the “study base” Case-control studies

28. Case-control studies Case-control studies

29. Population at risk (N=200)         Case-control studies

30. Week 1         O O Case-control studies

31. Week 2 O         O O O O Case-control studies

32. Week 3 O         O O O O O O Case-control studies

33. Incidence rate(“incidence density”) Number of new casesIR = ––––––––––––––––––– Population time Case-control studies

34. Incidence rate(“incidence density”) Number of new cases 7IR = ––––––––––––––––––– = –––––– Population time ? Case-control studies

35. Incidence rate(“incidence density”) Population time at risk: 200 people for 3 weeks = 600 person-wks But 2 people became cases in 1st week 3 people became cases in 2nd week 2 people became cases in 3rd week Only 193 people at risk for 3 weeks Case-control studies

36. Incidence rate(“incidence density”) Assume that: 2 people who became cases in 1st week were at risk for 0.5 weeks each = 2 @ 0.5 = 1.0 3 people who became cases in 2nd week were at risk for 1.5 weeks each = 3 @ 1.5 = 4.5 2 people who became cases in 3rd week were at risk for 2.5 weeks each = 2 @ 2.5 = 5.0 Case-control studies

37. Incidence rate(“incidence density”) Total population-time = Cases occuring during week 1: 1.0 p-w Cases occuring during week 2: 4.5 p-w Cases occuring during week 3: 5.0 p-w Non-cases: 193 x 3 = 579.0 p-w 589.5 p-w Case-control studies

38. Incidence rate(“incidence density”) Number of new casesIR = ––––––––––––––––––– Population time 7 IR = –––––– = 0.0119 cases / person-wk 589.5 average over 3 weeks Case-control studies

39. Incidence proportion(“cumulative incidence”) Number of new casesCI = ––––––––––––––––––– Population at risk Case-control studies

40. Incidence proportion(“cumulative incidence”) Number of new casesCI = ––––––––––––––––––– Population at risk 73-week CI = –––– = 0.035 200 Case-control studies

41. Can estimate incidence in people who are “exposed”    O Week 1 Case-control studies

42. Can estimate incidence in people who are “exposed” O    O O Week 2 Case-control studies

43. Can estimate incidence in people who are “exposed” O    O O O Week 3 Case-control studies

44. Can estimate incidence in people who are “unexposed” Week 1      O Case-control studies

45. Can estimate incidence in people who are “unexposed” Week 2      O O Case-control studies

46. Can estimate incidence in people who are “unexposed” Week 3      O O O Case-control studies

47. Entire population, week 1    O      O Case-control studies

48. Entire population, week 2 O    O O      O O Case-control studies

49. Entire population, week 3 O    O O O      O O O Case-control studies

50. Incidence rate(“incidence density”) Number of new casesIR = ––––––––––––––––––– Population time Case-control studies