标题： 临床试验中缺失连续结果的参数方法与机器学习多重插补比较 显示英文标题

标题： Comparison of Parametric versus Machine-learning Multiple Imputation in Clinical Trials with Missing Continuous Outcomes

摘要： 使用灵活的机器学习（ML）模型在多重插补（MI）框架内生成缺失数据的插补值，最近在观察性研究中越来越受到关注。 对于随机对照试验（RCT），尚不清楚ML方法在MI中的推断是否有效，以及在复杂的生成机制下是否优于参数化MI方法。 我们在具有不完整连续结果但完全观察协变量的RCT环境中进行了两次模拟。 我们比较了完整案例、标准MI（MI-norm）、预测均值匹配的MI（MI-PMM）以及基于ML的MI方法，包括分类和回归树（MI-CART）、随机森林（MI-RF）和SuperLearner，当结果完全随机缺失或在治疗/协变量条件下随机缺失时。 第一个模拟探讨了在协变量-治疗交互存在与否的情况下非线性协变量-结果关系。 第二个模拟探讨了偏斜的重复测量，这是由一个具有数字结果的试验所激发的。 在没有交互作用的情况下，我们发现完整案例可以提供可靠的推断； MI-norm表现相似，除非缺失依赖于协变量。 在特定的非线性情况下，ML方法可以比完整案例和MI-norm产生更小的均方误差，但在其他情况下提供不可靠的推断。 MI-PMM在多个情况下可能导致不可靠的推断。 在存在复杂治疗-协变量交互作用的情况下，分别按组进行MI，无论是使用MI-norm、MI-RF还是MI-CART，在分析模型忽略交互作用时，其推断效果与完整案例相比具有可比性或更好的性质。 对于ML方法，我们观察到在应用Rubin规则时，估计效应及其标准误差的偏差方面出现不可靠的推断。 显示英文摘要

摘要： The use of flexible machine-learning (ML) models to generate imputations of missing data within the framework of Multiple Imputation (MI) has recently gained traction, particularly in observational settings. For randomised controlled trials (RCTs), it is unclear whether ML approaches to MI provide valid inference, and whether they outperform parametric MI approaches under complex data generating mechanisms. We conducted two simulations in RCT settings that have incomplete continuous outcomes but fully observed covariates. We compared Complete Cases, standard MI (MI-norm), MI with predictive mean matching (MI-PMM) and ML-based approaches to MI, including classification and regression trees (MI-CART), Random Forests (MI-RF) and SuperLearner when outcomes are missing completely at random or missing at random conditional on treatment/covariate. The first simulation explored non-linear covariate-outcome relationships in the presence/absence of covariate-treatment interactions. The second simulation explored skewed repeated measures, motivated by a trial with digital outcomes. In the absence of interactions, we found that Complete Cases yields reliable inference; MI-norm performs similarly, except when missingness depends on the covariate. ML approaches can lead to smaller mean squared error than Complete Cases and MI-norm in specific non-linear settings, but provide unreliable inference for others. MI-PMM can lead to unreliable inference in several settings. In the presence of complex treatment-covariate interactions, performing MI separately by arm, either with MI-norm, MI-RF or MI-CART, provides inference that has comparable or with better properties compared to Complete Cases when the analysis model omits the interaction. For ML approaches, we observed unreliable inference in terms of bias in the estimated effect and/or its standard error when Rubin's Rules are implemented.