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The performance of multiple imputation (MI) models may be enhanced using auxiliary covariates (AC). Data-adaptive approaches to identify ACs for MI models are not well understood.
This presentation develops and compares high-dimensional MI (HDMI) approaches that leverage structured and natural language processing-derived ACs. It investigates whether HDMI can increase efficiency and decrease bias in real-world evidence studies with partially observed confounders. It was presented at the 2024 ISPE Annual Meeting.
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Contributors
Janick Weberpals, Pamela A. Shaw, Kueiyu Joshua Lin, Richard Wyss, Joseph M Plasek, Li Zhou, Kerry Ngan, Thomas DeRamus, Sudha R. Raman, Bradley G. Hammill, Hana Lee, Darren Toh, John G. Connolly, Kimberly J. Dandreo, Fang Tian, Wei Liu, Jie Li, José J. Hernández-Muñoz, Sebastian Schneeweiss, Rishi J. Desai