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：本研究针对条件逻辑回归（CLR）提出了一种改进方法，适用于关注参数为二元处理对二元响应中积极水平对数几率产生加性效应的场景。我们的改进核心在于：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。该先验随后应用于仅针对不一致对执行的CLR分析中。相较于传统CLR方法，本方法在样本量较小及非线性对数几率响应模型中的功效提升最为显著。相关算法已集成至名为bclogit的优化R软件包中发布。