A flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data.

ABSTRACT:

Background

Prior work has shown that combining bootstrap imputation with tree-based machine learning variable selection methods can provide good performances achievable on fully observed data when covariate and outcome data are missing at random (MAR). This approach however is computationally expensive, especially on large-scale datasets.

Methods

We propose an inference-based method, called RR-BART, which leverages the likelihood-based Bayesian machine learning technique, Bayesian additive regression trees, and uses Rubin's rule to combine the estimates and variances of the variable importance measures on multiply imputed datasets for variable selection in the presence of MAR data. We conduct a representative simulation study to investigate the practical operating characteri

SUBMITTER: Lin JJ 

PROVIDER: S-EPMC9066834 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature