Missing data is a challenge when developing, validating and deploying clinical prediction models (CPMs). Traditionally, decisions concerning missing data handling during CPM development and validation havent accounted for whether missingness is allowed at deployment. We hypothesised that the missing data approach used during model development should optimise model performance upon deployment, whilst the approach used during model validation should yield unbiased predictive performance estimates upon deployment; we term this compatibility. We aimed to determine which combinations of missing data handling methods across the CPM life cycle are compatible. We considered scenarios where CPMs are intended to be deployed with missing data allowed or not, and we evaluated the impact of that choice on earlier modelling decisions. Through a simulation study and an empirical analysis of thoracic surgery data, we compared CPMs developed and validated using combinations of complete case analysis, mean imputation, single regression imputation, multiple imputation, and pattern sub-modelling. If planning to deploy a CPM without allowing missing data, then development and validation should use multiple imputation when required. Where missingness is allowed at deployment, the same imputation method must be used during development and validation. Commonly used combinations of missing data handling methods result in biased predictive performance estimates.

翻译：缺失数据是开发、验证和部署临床预测模型（CPMs）时面临的挑战。传统上，在CPM开发和验证阶段关于缺失数据处理的决策并未考虑部署阶段是否允许数据缺失。我们假设：模型开发阶段使用的缺失数据方法应优化部署时的模型性能，而模型验证阶段使用的方法应在部署时产生无偏的预测性能估计——我们将此定义为兼容性。本研究旨在确定CPM生命周期中哪些缺失数据处理方法的组合具有兼容性。我们考虑了CPMs部署时允许或不允许数据缺失的场景，并评估该选择对早期建模决策的影响。通过模拟研究和胸外科手术数据的实证分析，我们比较了使用完整案例分析、均值插补、单一回归插补、多重插补和模式子建模等不同组合方法开发与验证的CPMs。若计划部署不允许数据缺失的CPM，则开发和验证阶段在需要时应使用多重插补方法。若部署阶段允许数据缺失，则开发与验证阶段必须使用相同的插补方法。常用的缺失数据处理方法组合会导致预测性能估计产生偏差。