NIH R01 · 2025
Project Summary This proposal will develop Bayesian machine learning approaches via Bayesian nonparametrics (BNP) to handle nonignorable missingness (in outcomes and covariates) and conduct causal inference for electronic health records (EHRs), to address missingness in multivariate longitudinal data, and for causal mediation problems. Missing data remains a problem in clinical studies and in particular, for studies using EHRs. In clinical studies, more effort is spent to try to minimize the amount of missingness, but it still remains a problem and missingness is a constant issue (and less controllable) in studies based on EHRs. In addition, there has been limited work on the use of…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.