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：近期发布的ICH E9临床试验估计量增补文件为精确定义待估计的治疗效应提供了框架，但关于估计方法的论述较为有限。本文针对2型糖尿病临床试验数据开展分析，聚焦随机化治疗效应，采用所谓假设性策略处理救援治疗与随机化治疗中断问题。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公式及G估计方法实现该效应估计，阐述了各方法的假设条件及R语言实现的技术细节。分析结果显示不同估计量获得的效应估计值与标准误总体接近。本文还讨论了选择估计方法时需考量的多重因素，包括计算效率、缺失数据处理策略、是否纳入并发事件后数据、是否以及如何调整额外时变混杂因素、是否以及如何对不同类型的并发事件进行独立建模。