1. BIOST 536 Lecture 12 1 Lecture 12 – Introduction to Matching

2. BIOST 536 Lecture 12 2 Conditional logistic regression

3. BIOST 536 Lecture 12 3 Conditional logistic regression

4. BIOST 536 Lecture 12 4 Conditional logistic regression

5. BIOST 536 Lecture 12 5 Conditional logistic regression

6. BIOST 536 Lecture 12 6 Example

7. BIOST 536 Lecture 12 7 Example Usual odds ratio and Mantel-Haenszel odds ratio adjusting for year of birth Standard logistic regression

8. BIOST 536 Lecture 12 8 Example Unconditional logistic regression adjusting for YOB

9. BIOST 536 Lecture 12 9 Example

10. BIOST 536 Lecture 12 10 Example Conditional logistic regression stratified on YOB with m cases : n controls for each YOB (“true stratification”) In all the analyses, the OR and 95% CI are about the same due to the close frequency matching

11. BIOST 536 Lecture 12 11 Conditional logistic regression

12. BIOST 536 Lecture 12 12 1-1 matching

13. BIOST 536 Lecture 12 13 1-1 matching

14. BIOST 536 Lecture 12 14 1-1 matching

15. BIOST 536 Lecture 12 15 1-1 matching

16. BIOST 536 Lecture 12 16 Example

17. BIOST 536 Lecture 12 17 Example Not really what we want since we want to retain the matching and compare Gall (case) vs Gall (control)

18. BIOST 536 Lecture 12 18 Example Use small trick to get case and control value on the same line for Gall bladder disease

19. BIOST 536 Lecture 12 19 Example Can use matched case-control command (mcc) Can get the OR easily and get confidence intervals and exact p- values based on the exact binomial distribution with null hypothesis p=0.50 and n = number discordant on exposure status Easier to just use conditional logistic regression

20. BIOST 536 Lecture 12 20 Example

21. BIOST 536 Lecture 12 21 Example

22. BIOST 536 Lecture 12 22 Example

23. BIOST 536 Lecture 12 23 Example

24. BIOST 536 Lecture 12 24 Example

25. BIOST 536 Lecture 12 25 1-m matching

26. BIOST 536 Lecture 12 26 1-m matching

27. BIOST 536 Lecture 12 27 1-m matching

28. BIOST 536 Lecture 12 28 Example

29. BIOST 536 Lecture 12 29 Example

30. BIOST 536 Lecture 12 30 Example

31. BIOST 536 Lecture 12 31 Example

32. BIOST 536 Lecture 12 32

33. BIOST 536 Lecture 12 33 Example

34. BIOST 536 Lecture 12 34 Example

35. BIOST 536 Lecture 12 35 Example

36. BIOST 536 Lecture 12 36 Example

37. BIOST 536 Lecture 12 37 Summary 1-1 matching case-control  Only sets where the covariate is different between case and control supply information about that covariate  Cannot get absolute probabilities, just conditional probabilities  Missing value for the case or control will cause loss of the set 1-m matching case-control  Only sets where the covariate is different between the case and at least one control will supply information about that covariate  Cannot get absolute probabilities, just conditional probabilities  Missing value for the case will cause loss of the set Can use Wald and LR tests as before for model fitting