Performance of Multiple Imputation Using Modern Machine Learning Methods in Electronic Health Records Data.

ABSTRACT:

Background

Missing data are common in studies using electronic health records (EHRs)-derived data. Missingness in EHR data is related to healthcare utilization patterns, resulting in complex and potentially missing not at random missingness mechanisms. Prior research has suggested that machine learning-based multiple imputation methods may outperform traditional methods and may perform well even in settings of missing not at random missingness.

Methods

We used plasmode simulations based on a nationwide EHR-derived de-identified database for patients with metastatic urothelial carcinoma to compare the performance of multiple imputation using chained equations, random forests, and denoising autoencoders in terms of bias and precision of hazard ratio estimates under varying pro

SUBMITTER: Getz K 

PROVIDER: S-EPMC12448120 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature