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2016 ; 17
(3
): 589-602
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Sequential BART for imputation of missing covariates
#MMPMID26980459
Xu D
; Daniels MJ
; Winterstein AG
Biostatistics
2016[Jul]; 17
(3
): 589-602
PMID26980459
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To conduct comparative effectiveness research using electronic health records
(EHR), many covariates are typically needed to adjust for selection and
confounding biases. Unfortunately, it is typical to have missingness in these
covariates. Just using cases with complete covariates will result in considerable
efficiency losses and likely bias. Here, we consider the covariates missing at
random with missing data mechanism either depending on the response or not.
Standard methods for multiple imputation can either fail to capture nonlinear
relationships or suffer from the incompatibility and uncongeniality issues. We
explore a flexible Bayesian nonparametric approach to impute the missing
covariates, which involves factoring the joint distribution of the covariates
with missingness into a set of sequential conditionals and applying Bayesian
additive regression trees to model each of these univariate conditionals. Using
data augmentation, the posterior for each conditional can be sampled
simultaneously. We provide details on the computational algorithm and make
comparisons to other methods, including parametric sequential imputation and two
versions of multiple imputation by chained equations. We illustrate the proposed
approach on EHR data from an affiliated tertiary care institution to examine
factors related to hyperglycemia.
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