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）指南中概述的估计目标框架明确了精确定义临床试验中待估效应所需的组成要素，其中包括如何处理基线后发生的"并发事件"（IEs）。在后期临床试验中，常采用治疗策略来处理"治疗终止"等并发事件，并针对所有结局（无论是否终止治疗）估计治疗效应。对于连续重复测量数据，通常利用终止治疗前后的所有观测数据，通过重复测量混合模型（MMRM）或多重插补（MI）处理缺失数据来估计此类效应。基础形式下，这两种估计方法均忽略分析中的治疗终止现象，因此当终止治疗患者的结局与仍接受治疗患者存在差异，且终止治疗患者的缺失数据更常见时，可能产生偏倚。为此，我们提出并评估了一套能够适应治疗终止前后结局差异的MI模型，并在规划呼吸系统疾病III期试验的背景下进行模型评估。研究表明，忽略治疗终止的分析可能引入显著偏倚，且有时会低估变异度。同时证明，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大。结论认为，部分MI模型优于忽略治疗终止的传统分析方法，但具体MI模型的选择需依据试验设计、疾病特征以及治疗终止后观测数据与缺失数据的分布情况而定。