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Group-sequential logrank methods for trial designs using bivariate non-competing event-time outcomes |
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| Authors: | Sugimoto Tomoyuki Hamasaki Toshimitsu Evans Scott R Halabi Susan |
| Institution: | 1.Graduate School of Data Science, Shiga University, 1-1-1 Banba, Hikone, Shiga, 522-8522, Japan ;2.Department of Data Science, National Cerebral and Cardiovascular Center, 5-7-1 Fujishiro-dai, Suita, Osaka, 565-8565, Japan ;3.Epidemiology and Biostatistics and the Center for Biostatistics, George Washington University, 6110 Executive Boulevard Suite 750, Rockville, MD, 20852-3943, USA ;4.Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27705, USA ; |
| Abstract: | 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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