High-dimensional multiple imputation for partially observed confounders including natural language processing-derived auxiliary covariates.

ABSTRACT: Multiple imputation (MI) models can be improved with auxiliary covariates (ACs), but their performance in high-dimensional data remains unclear. We aimed to develop and compare high-dimensional MI (HDMI) methods using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation with acute kidney injury as outcome and simulated 100 cohorts with a null treatment effect, incorporating creatinine labs, atrial fibrillation (AFib), and other investigator-derived confounders in the outcome generation. Missingness was imposed on creatinine based on creatinine itself and AFib. Different HDMI candidate ACs were created using structured and NLP-derived features, and we mimicked scenarios where AFib was unobserved by omi

SUBMITTER: Weberpals J 

PROVIDER: S-EPMC12780782 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature