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.
摘要：
小心处理缺失的数据对于确保临床预测模型的开发至关重要，已验证，并以稳健的方式实施。我们确定了在评估用于处理验证和实施过程中缺失数据的不同方法组合的预测性能时的偏差。我们发现了四种策略在整个模型管道中兼容，并为在不同错误机制下处理模型验证和实施之间的缺失数据提供了建议。
