标题： 使用治疗策略处理的二元结果的离治疗序贯多重插补方法来应对治疗过程中的事件 显示英文标题

标题： Using off-treatment sequential multiple imputation for binary outcomes to address intercurrent events handled by a treatment policy strategy

摘要： 估计量框架提出了不同的策略来处理后续事件。 治疗策略似乎是最受青睐的，因为它与附加前的意向性治疗原则紧密一致。 理想情况下，应收集所有患者的所有数据，然而，在现实中，患者可能会退出研究，导致数据缺失。 这需要作为估计的一部分来处理。 一些研究领域已经开展了探索，研究在使用治疗策略处理后续事件时估计量的模型，但对于二元终点的研究有限。 我们探讨了不同的检索脱落模型，在后续事件发生后，可以利用观察到的数据来多重填补后续事件发生后的缺失数据。 我们将所提出的模型与一个简单的插补模型进行比较，该模型不对后续事件前后数据做出区分，并通过模拟研究评估不同的统计特性。 然后，我们提供了一个示例，说明检索脱落模型如何在类风湿性关节炎的III期临床试验中实际应用。 从所探讨的模型来看，我们认为一个包含是否发生了后续事件的指示变量的简单检索脱落模型是最实用的选择。 然而，这些模型要有效运行，至少需要50%的后续事件后观察数据。 因此，在实践中实施该模型的适用性将取决于可用的后续事件后观察数据和缺失数据的数量。 显示英文摘要

摘要： The estimand framework proposes different strategies to address intercurrent events. The treatment policy strategy seems to be the most favoured as it is closely aligned with the pre-addendum intention-to-treat principle. All data for all patients should ideally be collected, however, in reality patients may withdraw from a study leading to missing data. This needs to be dealt with as part of the estimation. Several areas of research have been conducted exploring models to estimate the estimand when intercurrent events are handled using a treatment policy strategy, however the research is limited for binary endpoints. We explore different retrieved dropout models, where post-intercurrent event, the observed data can be used to multiply impute the missing post-intercurrent event data. We compare our proposed models to a simple imputation model that makes no distinction between the pre- and post-intercurrent event data, and assess varying statistical properties through a simulation study. We then provide an example how retrieved dropout models were used in practice for Phase 3 clinical trials in rheumatoid arthritis. From the models explored, we conclude that a simple retrieved dropout model including an indicator for whether or not the intercurrent event occurred is the most pragmatic choice. However, at least 50% of observed post-intercurrent event data is required for these models to work well. Therefore, the suitability of implementing this model in practice will depend on the amount of observed post-intercurrent event data available and missing data.