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.Comment: 36 pages, 5 figure