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Compatibility in Missing Data Handling Across the Prediction Model Pipeline: A Simulation Study
Antonia Tsvetanova, Matthew Sperrin, David Jenkins, Niels Peek, Iain Buchan, Stephanie Hyland, Glen Martin
1476 - 1477
10.3233/SHTI231252
Research Article
Studies in Health Technology and Informatics
Volume 310: MEDINFO 2023 — The Future Is Accessible
Abstract

Careful handling of missing data is crucial to ensure that clinical prediction models are developed, validated, and implemented in a robust manner. We determined the bias in estimating predictive performance of different combinations of approaches for handling missing data across validation and implementation. We found four strategies that are compatible across the model pipeline and have provided recommendations for handling missing data between model validation and implementation under different missingness mechanisms.

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