Bayesian Nonparametric Model for Heterogeneous Treatment Effects With Zero-Inflated Data.

One goal of precision medicine is to develop effective treatments for patients by tailoring to their individual demographic, clinical, and/or genetic characteristics. To achieve this goal, statistical models must be developed that can identify and evaluate potentially heterogeneous treatment effects in a robust manner. The oft-cited existing methods for assessing treatment effect heterogeneity are based upon parametric models with interactions or conditioning on covariate values, the performance of which is sensitive to the omission of important covariates and/or the choice of their values. We propose a new Bayesian nonparametric (BNP) method for estimating heterogeneous causal effects in studies with zero-inflated outcome data, which arise commonly in health-related studies. We employ the enriched Dirichlet process (EDP) mixture in our BNP approach, establishing a connection between an outcome DP mixture and a covariate DP mixture. This enables us to estimate posterior distributions concurrently, facilitating flexible inference regarding individual causal effects. We show in a set of simulation studies that the proposed method outperforms two other BNP methods in terms of bias and mean squared error (MSE) of the conditional average treatment effect estimates. In particular, the proposed model has the advantage of appropriately reflecting uncertainty in regions where the overlap condition is violated compared to other competing models. We apply the proposed method to a study of the relationship between heart radiation dose parameters and the blood level of high-sensitivity cardiac troponin T (hs-cTnT) to examine if the effect of a high mean heart radiation dose on hs-cTnT varies by baseline characteristics.