Conditional logistic regression is an extension of logistic regression commonly used in Epidemiology for matched case-control studies. It accounts for the matching by conditioning on the matched sets, allowing for the estimation of the association between exposure and outcome while controlling for the confounding variables that were used for matching.
In epidemiological studies, especially case-control studies, matching is often employed to control for confounding factors. For instance, cases (people with the disease) might be matched to controls (people without the disease) based on age, sex, or other variables. Regular logistic regression does not account for this matching, which can lead to biased estimates. Conditional logistic regression corrects for this by focusing on the differences within matched sets.
Conditional logistic regression works by stratifying the data according to the matched sets. Within each stratum, it estimates the likelihood of exposure given the outcome, conditional on the stratum. It essentially compares each case to its matched controls, controlling for the matching variables. This method reduces the bias associated with confounding variables.
Conditional logistic regression is particularly useful when: - You have a matched case-control study design. - The matching variables are strong confounders. - You have small to moderate-sized datasets where the matching controls for numerous confounders.
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).
