The ICH E9(R1) Addendum (International Council for Harmonization 2019) suggests treatment-policy as one of several strategies for addressing intercurrent events such as treatment withdrawal when defining an estimand. This strategy requires the monitoring of patients and collection of primary outcome data following termination of randomized treatment. However, when patients withdraw from a study before nominal completion this creates true missing data complicating the analysis. One possible way forward uses multiple imputation to replace the missing data based on a model for outcome on and off treatment prior to study withdrawal, often referred to as retrieved dropout multiple imputation. This article explores a novel approach to parameterizing this imputation model so that those parameters which may be difficult to estimate have mildly informative Bayesian priors applied during the imputation stage. A core reference-based model is combined with a compliance model, using both on- and off- treatment data to form an extended model for the purposes of imputation. This alleviates the problem of specifying a complex set of analysis rules to accommodate situations where parameters which influence the estimated value are not estimable or are poorly estimated, leading to unrealistically large standard errors in the resulting analysis.

翻译：ICH E9(R1)附录（国际协调委员会，2019年）建议将治疗策略作为定义估计目标时处理治疗退出等并发事件的多种策略之一。该策略要求在随机治疗终止后持续监测患者并收集主要结局数据。然而，当患者在名义研究完成前退出时，会产生真实的缺失数据，使分析复杂化。一种可行的解决方案是采用多重插补，基于退出研究前治疗期与停药期的结局模型替换缺失数据，这通常被称为"回收退出者多重插补"。本文探索了一种对该插补模型进行参数化的新方法，使得那些难以估计的参数在插补阶段应用轻度信息性贝叶斯先验。通过将核心参考模型与依从性模型相结合，利用治疗期与停药期数据构建用于插补的扩展模型。这缓解了需制定复杂分析规则以适应参数不可估或估计不良（导致分析结果出现不切实际的大标准误）的问题。