Department of Biometrics, Eli Lilly and Company, Indianapolis, Indiana, USA.
Biostatistics and Programming, Sanofi, Bridgewater, New Jersey, USA.
Pharm Stat. 2021 Jan;20(1):55-67. doi: 10.1002/pst.2054. Epub 2020 Aug 10.
Intercurrent events (ICEs) and missing values are inevitable in clinical trials of any size and duration, making it difficult to assess the treatment effect for all patients in randomized clinical trials. Defining the appropriate estimand that is relevant to the clinical research question is the first step in analyzing data. The tripartite estimands, which evaluate the treatment differences in the proportion of patients with ICEs due to adverse events, the proportion of patients with ICEs due to lack of efficacy, and the primary efficacy outcome for those who can adhere to study treatment under the causal inference framework, are of interest to many stakeholders in understanding the totality of treatment effects. In this manuscript, we discuss the details of how to estimate tripartite estimands based on a causal inference framework and how to interpret tripartite estimates through a phase 3 clinical study evaluating a basal insulin treatment for patients with type 1 diabetes.
在任何规模和持续时间的临床试验中，伴随事件（ICEs）和缺失值都是不可避免的，这使得在随机临床试验中评估所有患者的治疗效果变得困难。定义与临床研究问题相关的适当估计量是分析数据的第一步。三方估计量评估了由于不良事件导致 ICEs 的患者比例、由于疗效不足导致 ICEs 的患者比例以及在因果推理框架下能够坚持研究治疗的患者的主要疗效结局之间的治疗差异，这引起了许多利益相关者对了解治疗效果全貌的兴趣。在本文中，我们讨论了如何基于因果推理框架估计三方估计量以及如何通过评估 1 型糖尿病患者基础胰岛素治疗的 3 期临床研究来解释三方估计的详细信息。
