Department of Biostatistics, University of Michigan, Ann Arbor, Michigan.
Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts.
Stat Med. 2019 Dec 30;38(30):5657-5669. doi: 10.1002/sim.8389. Epub 2019 Nov 15.
Few methods are currently available for group sequential analysis of recurrent events data subject to a terminal event in the clinical trial setting. This research helps fill this gap by developing a completely nonparametric group sequential monitoring procedure for use with the two-sample Tayob and Murray statistic. Advantages of the Tayob and Murray statistic include high power to detect treatment differences when there is correlation between recurrent event times or between recurrent and terminal events in an individual. This statistic does not suffer bias from dependent censoring, regardless of the correlation between event times in an individual. This manuscript briefly reviews the Tayob and Murray statistic, develops and describes how to use methods for its group sequential analysis, and through simulation, compares its operating characteristics with those of Cook and Lawless, which is currently in use as the only available nonparametric method for group sequential analysis of recurrent event data. The merits of our proposed approach are most clearly demonstrated when gap times between recurrent events are correlated; when gap times between events are independent, the Cook and Lawless method is difficult to beat. Simulations demonstrate that as correlation between recurrent event times grows, the reduction in power using the Cook and Lawless approach is substantial when compared to our method. Finally, we use our method to analyze recurrent acute exacerbation outcomes from the azithromycin in chronic obstructive pulmonary disease trial.
目前在临床试验中，针对终端事件的复发性事件数据，可用的群组序贯分析方法较少。本研究通过开发一种完全非参数的两样本 Tayob 和 Murray 统计量的群组序贯监测程序，有助于填补这一空白。Tayob 和 Murray 统计量的优势包括当个体中复发性事件时间之间或复发性事件和终端事件之间存在相关性时，具有检测治疗差异的高功效。无论个体中事件时间之间是否存在相关性，该统计量都不会受到依赖性删失的偏差影响。本文简要回顾了Tayob 和 Murray 统计量，开发并描述了如何使用其群组序贯分析方法，并通过模拟，将其与目前唯一可用的复发性事件数据群组序贯分析的非参数方法 Cook 和 Lawless 进行比较。当复发性事件之间的间隔时间相关时，我们提出的方法的优点最为明显；当事件之间的间隔时间独立时，Cook 和 Lawless 方法很难被击败。模拟结果表明，随着复发性事件时间之间的相关性增加，与我们的方法相比，使用 Cook 和 Lawless 方法的功效大大降低。最后，我们使用我们的方法分析慢性阻塞性肺疾病中阿奇霉素的复发性急性加重结局。
