The implementation of conditional logistic regression can be done using statistical software like R, SAS, or Stata. For example, in R, you can use the `clogit` function from the `survival` package. The basic syntax involves specifying the outcome variable, the exposure variable, and the strata (matched sets).
Example in R: R library(survival) model Advantages of Conditional Logistic Regression - Control for Confounding: It effectively controls for confounding variables that were used for matching. - Efficiency: It is more efficient than unconditional logistic regression in matched case-control studies. - Interpretability: The odds ratios derived from conditional logistic regression are directly interpretable within the context of the matched study design.
