Bootstrapping followed by deterministic imputation, with all missing values imputed and the outcome excluded, produces less biased AUC, Brier score, and individual risk predictions than complete case analysis in simulated clinical prediction settings.
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Combining missing data imputation and internal validation in clinical risk prediction models
Bootstrapping followed by deterministic imputation, with all missing values imputed and the outcome excluded, produces less biased AUC, Brier score, and individual risk predictions than complete case analysis in simulated clinical prediction settings.