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

ABSTRACT: 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

SUBMITTER: Kim C 

PROVIDER: S-EPMC11834965 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature