Group-sequential logrank methods for trial designs using bivariate non-competing event-time outcomes

被引：0
|
作者：
Tomoyuki Sugimoto
Toshimitsu Hamasaki
Scott R. Evans
Susan Halabi
机构：
[1] Shiga University,Graduate School of Data Science
[2] National Cerebral and Cardiovascular Center,Department of Data Science
[3] George Washington University,Epidemiology and Biostatistics and the Center for Biostatistics
[4] Duke University School of Medicine,Department of Biostatistics and Bioinformatics
来源：
Lifetime Data Analysis | 2020年 / 26卷
关键词：
Bivariate dependence; Error-spending method; Independent censoring; Logrank statistic; Non-fatal events; Normal approximation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We discuss the multivariate (2L-variate) correlation structure and the asymptotic distribution for the group-sequential weighted logrank statistics formulated when monitoring two correlated event-time outcomes in clinical trials. The asymptotic distribution and the variance–covariance for the 2L-variate weighted logrank statistic are derived as available in various group-sequential trial designs. These methods are used to determine a group-sequential testing procedure based on calendar times or information fractions. We apply the theoretical results to a group-sequential method for monitoring a clinical trial with early stopping for efficacy when the trial is designed to evaluate the joint effect on two correlated event-time outcomes. We illustrate the method with application to a clinical trial and describe how to calculate the required sample sizes and numbers of events.
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页码：266 / 291
页数：25
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