Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Clin Trials. 2024 Oct;21(5):595-603. doi: 10.1177/17407745241265628. Epub 2024 Aug 8.
Composite endpoints defined as the time to the earliest of two or more events are often used as primary endpoints in clinical trials. Component-wise censoring arises when different components of the composite endpoint are censored differently. We focus on a composite of death and a non-fatal event where death time is right censored and the non-fatal event time is interval censored because the event can only be detected during study visits. Such data are most often analysed using methods for right censored data, treating the time the non-fatal event was first detected as the time it occurred. This can lead to bias, particularly when the time between assessments is long. We describe several approaches for estimating the event-free survival curve and the effect of treatment on event-free survival via the hazard ratio that are specifically designed to handle component-wise censoring. We apply the methods to a randomized study of breastfeeding versus formula feeding for infants of mothers infected with human immunodeficiency virus.
复合终点定义为最早发生的两个或多个事件的时间,通常被用作临床试验的主要终点。当复合终点的不同组成部分以不同的方式进行删失时,就会出现按组成部分删失。我们关注的是死亡和非致死事件的复合终点,其中死亡时间是右删失的,非致死事件时间是区间删失的,因为只有在研究访视期间才能检测到该事件。此类数据通常使用右删失数据的方法进行分析,将首次检测到非致死事件的时间视为事件发生的时间。这可能会导致偏差,尤其是当评估之间的时间间隔较长时。我们描述了几种通过危险比估计无事件生存曲线和治疗对无事件生存影响的方法,这些方法专门用于处理按组成部分删失。我们将这些方法应用于一项针对母亲感染人类免疫缺陷病毒的婴儿进行母乳喂养与配方奶喂养的随机研究。