Conditional logistic regression
Conditional logistic regression is an extension of logistic regression that allows to take into account stratification and matching. Its main field of application is observational studies and in particular epidemiology. It was designed in 1978 by Norman Breslow, Nicholas Day, K. T. Halvorsen, Ross L. Prentice and C. Sabai.[1] It is the most flexible and general procedure for matched data.
Motivation
Observational studies use stratification or matching as a way to control for bias. Several tests existed before conditional logistic regression for matched data as shown in the related tests section. However, they did not allow for the analysis of continuous predictors with arbitrary strata size. All of those procedures also lack the flexibility of conditional logistic regression and in particular the possibility to control for covariates.
Logistic regression can take into account stratification by having a different constant term for each strata.
Conditional likelihood
Implementation
Conditional logistic regression is available in R as the function clogit in the survival package. It is in the survival package because the log likelihood of a conditional logistic model is the same as the log likelihood of a Cox model with a particular data structure.[2]
Related tests
Notes
- ^ Breslow, N. E., Day, N. E., Halvorsen, K. T., Prentice, R. L., & Sabai, C. (1978). "Estimation of multiple relative risk functions in matched case-control studies". American Journal of Epidemiology. 108 (4): 299–307.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ^ Lumley, Thomas. "R documentation Conditional logistic regression". Retrieved November 3, 2016.