Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on Electronic Health Record Data.

ABSTRACT:

Background

Missing data in electronic health records are highly prevalent and result in analytical concerns such as heterogeneous sources of bias and loss of statistical power. One simple analytic method for addressing missing or unknown covariate values is to treat missingness for a particular variable as a category onto itself, which we refer to as the missing indicator method. For cross-sectional analyses, recent work suggested that there was minimal benefit to the missing indicator method; however, it is unclear how this approach performs in the setting of longitudinal data, in which correlation among clustered repeated measures may be leveraged for potentially improved model performance.

objectives

This study aims to conduct a simulation study to evaluate whether the mi

SUBMITTER: Ehrig M 

PROVIDER: S-EPMC11924964 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature