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.

翻译：本文针对条件逻辑回归（CLR）提出了一种改进方法，适用于研究场景中关注参数为二元处理对二元响应正水平对数几率（log-odds）的加性效应。我们的改进方法在于：利用通常被舍弃的一致响应对观测中关于干扰控制协变量的信息，构建其系数的信息性先验分布。该先验随后被应用于仅针对不一致对运行的条件逻辑回归中。相较于传统CLR，本方法在样本量较小及非线性正响应对数几率模型中的功效提升最为显著。相关方法已通过优化的R软件包bclogit发布。