Missing predictor data increases the minimum sample size needed for stable, well-calibrated clinical prediction models, with context-specific inflation factors up to 2x, via adaptations to posterior sampling calculations.
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Complete-case TMLE that includes an outcome-missingness model shows lower bias and greater robustness to positivity violations than multiple imputation approaches, while MI with CART yields lower RMSE and nominal coverage in simulations based on five missingness DAGs and a real epidemiological data.
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Incorporating Missing Data Considerations into Sample Size Calculations for Developing Clinical Prediction Models
Missing predictor data increases the minimum sample size needed for stable, well-calibrated clinical prediction models, with context-specific inflation factors up to 2x, via adaptations to posterior sampling calculations.
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Causal Effect Estimation with TMLE: Handling Missing Data and Near-Violations of Positivity
Complete-case TMLE that includes an outcome-missingness model shows lower bias and greater robustness to positivity violations than multiple imputation approaches, while MI with CART yields lower RMSE and nominal coverage in simulations based on five missingness DAGs and a real epidemiological data.