Epidemiological approaches for examining human health responses to environmental exposures in observational studies often control for confounding by implementing clever matching schemes and using statistical methods based on conditional likelihood. Nonparametric regression models have surged in popularity in recent years as a tool for estimating individual-level heterogeneous effects, which provide a more detailed picture of the exposure-response relationship but can also be aggregated to obtain improved marginal estimates at the population level. In this work we incorporate Bayesian additive regression trees (BART) into the conditional logistic regression model to identify heterogeneous effects of environmental exposures in a case-crossover design. Conditional logistic BART (CL-BART) utilizes reversible jump Markov chain Monte Carlo to bypass the conditional conjugacy requirement of the original BART algorithm. Our work is motivated by the growing interest in identifying subpopulations more vulnerable to environmental exposures. We apply CL-BART to a study of the impact of heatwaves on people with Alzheimer's Disease in California and effect modification by other chronic conditions. Through this application, we also describe strategies to examine heterogeneous odds ratios through variable importance, partial dependence, and lower-dimensional summaries. CL-BART is available in the clbart R package.

翻译：流行病学方法在观察性研究中评估环境暴露对人类健康的影响时，常通过巧妙匹配方案和基于条件似然的统计方法控制混杂因素。近年来，非参数回归模型作为估计个体水平异质性效应的工具日益流行，这些模型能更细致地描述暴露-反应关系，同时可聚合生成人群层面更优的边缘估计。本文在条件逻辑回归模型中引入贝叶斯加性回归树（BART），用于识别病例交叉设计中环境暴露的异质性效应。条件逻辑BART（CL-BART）采用可逆跳跃马尔可夫链蒙特卡洛方法，绕过了原始BART算法对条件共轭性的要求。本研究的动因源于对识别环境暴露易感亚人群日益增长的研究兴趣。我们将CL-BART应用于研究热浪对加利福尼亚州阿尔茨海默病患者的影响，以及其他慢性疾病对效应的修饰作用。通过该应用，我们还描述了通过变量重要性、部分依赖性和低维汇总统计检验异质性比值比的策略。CL-BART已在clbart R软件包中实现。