Estimating Heterogeneous Exposure Effects in the Case-Crossover Design Using BART

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 exposure effects in a case-crossover design. Conditional logistic BART (CL-BART) uses 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 heat waves 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. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

在观察性研究中，用于探究人类健康对环境暴露响应的流行病学研究方法，通常通过设计精巧的匹配方案与基于条件似然的统计方法来控制混杂偏倚。近年来，非参数回归模型作为估计个体层面异质性效应的工具愈发流行——这类模型不仅能更细致地刻画暴露-反应关系，还可通过聚合得到人群层面更精准的边际估计值。本研究将贝叶斯加性回归树（Bayesian Additive Regression Trees, BART）融入条件logistic回归模型，以识别病例交叉设计中的异质性暴露效应。条件logistic贝叶斯加性回归树（CL-BART）采用可逆跳变马尔可夫链蒙特卡洛方法，规避了原始BART算法的条件共轭性要求。本研究的初衷源于学界对识别更易受环境暴露影响的亚人群的日益浓厚的研究兴趣。我们将CL-BART应用于一项针对加州热浪对阿尔茨海默病患者影响的研究，并探究其他慢性疾病对该效应的修饰作用。通过该应用实例，我们还阐述了基于变量重要性、偏依赖与低维汇总分析异质性比值比的策略。本文的补充材料可在线获取，其中包含了复现本研究所需材料的标准化说明。