The estimands framework outlined in ICH E9 (R1) describes the components needed to precisely define the effects to be estimated in clinical trials, which includes how post-baseline "intercurrent" events (IEs) are to be handled. In late-stage clinical trials, it is common to handle intercurrent events like "treatment discontinuation" using the treatment policy strategy and target the treatment effect on all outcomes regardless of treatment discontinuation. For continuous repeated measures, this type of effect is often estimated using all observed data before and after discontinuation using either a mixed model for repeated measures (MMRM) or multiple imputation (MI) to handle any missing data. In basic form, both of these estimation methods ignore treatment discontinuation in the analysis and therefore may be biased if there are differences in patient outcomes after treatment discontinuation compared to patients still assigned to treatment, and missing data being more common for patients who have discontinued treatment. We therefore propose and evaluate a set of MI models that can accommodate differences between outcomes before and after treatment discontinuation. The models are evaluated in the context of planning a phase 3 trial for a respiratory disease. We show that analyses ignoring treatment discontinuation can introduce substantial bias and can sometimes underestimate variability. We also show that some of the MI models proposed can successfully correct the bias but inevitably lead to increases in variance. We conclude that some of the proposed MI models are preferable to the traditional analysis ignoring treatment discontinuation, but the precise choice of MI model will likely depend on the trial design, disease of interest and amount of observed and missing data following treatment discontinuation.

翻译：ICH E9（R1）中概述的估计量框架描述了精确定义临床试验中待评估效应所需的组成部分，其中包括如何处理基线后"并发事件"（IEs）。在后期临床试验中，通常采用治疗策略处理"治疗中断"等并发事件，并针对所有结局（无论是否发生治疗中断）评估治疗效应。对于连续重复测量数据，此类效应通常利用治疗中断前后所有观测数据通过重复测量混合模型（MMRM）或多重插补（MI）处理缺失数据进行估计。在基础形式下，这两种估计方法在分析中忽略了治疗中断，因此若治疗中断后患者结局与仍接受治疗的患者存在差异，且中断治疗患者的缺失数据更普遍时，可能产生偏倚。为此，我们提出并评估了一组可适应治疗中断前后结局差异的MI模型。这些模型在呼吸系统疾病III期临床试验规划背景下进行评估。研究表明，忽略治疗中断的分析可能引入显著偏倚，并有时低估变异性。部分提出的MI模型可成功校正偏倚，但不可避免地导致方差增加。我们得出结论：部分提出的MI模型优于忽略治疗中断的传统分析，但具体MI模型的选择可能取决于试验设计、目标疾病以及治疗中断后观测数据和缺失数据的数量。