论文标题
在因果推理框架下使用依从性因果估计量实施三方估计。
Implementation of Tripartite Estimands Using Adherence Causal Estimators Under the Causal Inference Framework
论文作者
论文摘要
在任何大小和持续时间的临床试验中，不可避免的事件（IC）和缺失值是不可避免的，因此很难评估随机临床试验中所有患者的治疗效果。定义与临床研究问题相关的适当估计是分析数据的第一步。三方估计值评估了由于缺乏疗效而导致的ICES患者比例的治疗差异，以及对于那些可以在因果推理框架下研究治疗的人的主要疗效结果的主要疗效结果对许多利益相关者感兴趣的人都在理解治疗效果的总体上都具有感兴趣。在本手稿中，我们讨论了如何基于因果推理框架估算三方估计的细节，以及如何通过3期临床研究来解释三方估计，以评估1型糖尿病患者的基础胰岛素治疗。
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.
