主 题：Estimating treatment effects with competing intercurrent events in randomized controlled trials
主讲人：加州大学伯克利分校丁鹏副教授
主持人：统计与数据科学学院林华珍教授
时间：2025年8月4日（周一）下午4-5点
地点：柳林校区弘远楼408会议室
主办单位：统计与数据科学学院和统计研究中心 科研处
主讲人简介：
丁鹏（Peng Ding）是加州大学伯克利分校统计系的副教授。他于2015年5月获得哈佛大学统计系博士学位，并在同年12月之前担任哈佛大学陈曾熙公共卫生学院流行病学系的博士后研究员。在此之前，他在北京大学获得了数学学士、经济学学士以及统计学硕士学位。
内容提要：
The analysis of randomized controlled trials is often complicated by intercurrent events (ICEs) – events that occur after treatment initiation and affect either the interpretation or existence of outcome measurements. Examples include treatment discontinuation or the use of additional med- ications. In two recent clinical trials for systemic lupus erythematosus with complications of ICEs, we classify the ICEs into two broad categories: treatment-related (e.g., treatment discontinuation due to adverse events or lack of efficacy) and treatment-unrelated (e.g., treatment discontinua- tion due to external factors such as pandemics or relocation). To define a clinically meaningful estimand, we adopt tailored strategies for each category of ICEs. For treatment-related ICEs, which are often informative about a patient’s outcome, we use the composite variable strategy that assigns an outcome value indicative of treatment failure. For treatment-unrelated ICEs, we apply the hypothetical strategy, assuming their timing is conditionally independent of the outcome given treatment and baseline covariates, and hypothesizing a scenario in which such events do not occur. A central yet previously overlooked challenge is the presence of competing ICEs, where the first ICE censors all subsequent ones. Despite its ubiquity in practice, this issue has not been explicitly recognized or addressed in previous data analyses due to the lack of rigorous statisti- cal methodology. In this paper, we propose a principled framework to formulate the estimand, establish its nonparametric identification and semiparametric estimation theory, and introduce weighting, outcome regression, and doubly robust estimators. We apply our methods to analyze the two systemic lupus erythematosus trials, demonstrating the robustness and practical utility of the proposed framework.
