biostudies-literatureLin JJNational Cancer InstituteNCI NIH HHSPatient-Centered Outcomes Research Institute132https://www.ebi.ac.uk/biostudies/studies/S-EPMC9066834biostudies-literatureUnknown22(1)<h4>Background</h4>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.<h4>Methods</h4>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 characteriBMC medical research methodologyA flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data.PMC9066834ME_2017C3_9041P30 CA196521R21 CA245855R21CA245855Huang CLin JJJiayi JGovindarajulu UHu LLawrence SfalseA flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data.<h4>Background</h4>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.<h4>Methods</h4>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 characteri2022-01-01T00:00:00Z2022 May2025-04-26T04:32:04.829Z2025-02-19T03:47:04.814ZS-EPMC90668343550897410.1186/s12874-022-01608-7