因果推断框架下使用粘附因果估计器实现三方估计
Implementation of Tripartite Estimands Using Adherence Causal Estimators Under the Causal Inference Framework
摘要：
在任何规模和持续时间的临床试验中，并发事件（ICE）和缺失值都是不可避免的，因此难以评估随机临床试验中所有患者的处理效应。定义与临床研究问题相关的适当被估量是分析数据的第一步。三方被估量评价了不良事件导致的ICE患者比例、缺乏疗效导致的ICE患者比例以及在因果推断框架下能够坚持研究治疗的患者的主要疗效结局的治疗差异，许多利益相关者对理解总体处理效应感兴趣。在本文中，我们讨论了如何基于因果推断框架估计三方被估量的细节，以及如何通过评价1型糖尿病患者基础胰岛素治疗的3期临床研究解释三方估计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
