Wang Chenguang, Scharfstein Daniel O, Colantuoni Elizabeth, Girard Timothy D, Yan Ying
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, U.S.A.
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, U.S.A.
Biometrics. 2017 Jun;73(2):431-440. doi: 10.1111/biom.12594. Epub 2016 Oct 17.
In randomized studies involving severely ill patients, functional outcomes are often unobserved due to missed clinic visits, premature withdrawal, or death. It is well known that if these unobserved functional outcomes are not handled properly, biased treatment comparisons can be produced. In this article, we propose a procedure for comparing treatments that is based on a composite endpoint that combines information on both the functional outcome and survival. We further propose a missing data imputation scheme and sensitivity analysis strategy to handle the unobserved functional outcomes not due to death. Illustrations of the proposed method are given by analyzing data from a recent non-small cell lung cancer clinical trial and a recent trial of sedation interruption among mechanically ventilated patients.
在涉及重症患者的随机研究中，由于患者错过门诊就诊、提前退出或死亡，功能结局往往无法观察到。众所周知，如果这些未观察到的功能结局处理不当，可能会产生有偏差的治疗比较结果。在本文中，我们提出了一种基于复合终点来比较治疗方法的程序，该复合终点结合了功能结局和生存两方面的信息。我们还提出了一种缺失数据插补方案和敏感性分析策略，以处理并非由死亡导致的未观察到的功能结局。通过分析来自最近一项非小细胞肺癌临床试验以及最近一项针对机械通气患者镇静中断试验的数据，对所提出的方法进行了说明。
