BIOST 536 Lecture 12 1 Lecture 12 – Introduction to Matching

BIOST 536 Lecture 12 2 Conditional logistic regression

BIOST 536 Lecture 12 3 Conditional logistic regression

BIOST 536 Lecture 12 4 Conditional logistic regression

BIOST 536 Lecture 12 5 Conditional logistic regression

BIOST 536 Lecture 12 6 Example

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

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

BIOST 536 Lecture 12 9 Example

BIOST 536 Lecture 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

BIOST 536 Lecture Conditional logistic regression

BIOST 536 Lecture matching

BIOST 536 Lecture matching

BIOST 536 Lecture matching

BIOST 536 Lecture matching

BIOST 536 Lecture Example

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

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

BIOST 536 Lecture 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

BIOST 536 Lecture Example

BIOST 536 Lecture Example

BIOST 536 Lecture Example

BIOST 536 Lecture Example

BIOST 536 Lecture Example

BIOST 536 Lecture m matching

BIOST 536 Lecture m matching

BIOST 536 Lecture m matching

BIOST 536 Lecture Example

BIOST 536 Lecture Example

BIOST 536 Lecture Example

BIOST 536 Lecture Example

BIOST 536 Lecture 12 32

BIOST 536 Lecture Example

BIOST 536 Lecture Example

BIOST 536 Lecture Example

BIOST 536 Lecture Example

BIOST 536 Lecture 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