Improved Conditional Logistic Regression using Information in Concordant Pairs with Software

TL;DR

This paper introduces an enhanced conditional logistic regression method that leverages information from concordant pairs to improve power, especially in small samples and nonlinear models, implemented in an R package.

Contribution

The authors develop a novel approach to improve CLR by incorporating information from concordant pairs as an informative prior, enhancing performance in small samples and nonlinear models.

Findings

Significant power improvements in small sample sizes.

Enhanced performance in nonlinear log-odds models.

Method implemented in the bclogit R package.

Abstract

We develop an improvement to conditional logistic regression (CLR) in the setting where the parameter of interest is the additive effect of binary treatment effect on log-odds of the positive level in the binary response. Our improvement is simply to use information learned above the nuisance control covariates found in the concordant response pairs' observations (which is usually discarded) to create an informative prior on their coefficients. This prior is then used in the CLR which is run on the discordant pairs. Our power improvements over CLR are most notable in small sample sizes and in nonlinear log-odds-of-positive-response models. Our methods are released in an optimized R package called bclogit.