The recently published ICH E9 addendum on estimands in clinical trials provides a framework for precisely defining the treatment effect that is to be estimated, but says little about estimation methods. Here we report analyses of a clinical trial in type 2 diabetes, targeting the effects of randomised treatment, handling rescue treatment and discontinuation of randomised treatment using the so-called hypothetical strategy. We show how this can be estimated using mixed models for repeated measures, multiple imputation, inverse probability of treatment weighting, G-formula and G-estimation. We describe their assumptions and practical details of their implementation using packages in R. We report the results of these analyses, broadly finding similar estimates and standard errors across the estimators. We discuss various considerations relevant when choosing an estimation approach, including computational time, how to handle missing data, whether to include post intercurrent event data in the analysis, whether and how to adjust for additional time-varying confounders, and whether and how to model different types of ICE separately.

翻译：近期发表的ICH E9增补文件为临床试验中治疗效应的精确定义提供了框架，但关于估计方法的论述较为有限。本文针对2型糖尿病临床试验进行分析，聚焦随机治疗效应，采用所谓假设性策略处理挽救性治疗和随机治疗终止情况。我们展示了如何通过重复测量混合模型、多重插补、逆概率治疗加权、G公式和G估计等方法进行估计，阐述了这些方法的假设条件及使用R软件包实施的技术细节。报告显示各估计方法的结果大致相似，标准误接近。我们讨论了选择估计方法时需考虑的多重因素，包括计算时间、缺失数据处理方式、是否纳入并发事件发生后的数据、是否及如何调整额外时变混杂因素，以及是否及如何对不同类型并发事件进行单独建模。