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

Tomoyuki Sugimoto (), Toshimitsu Hamasaki (), Scott R. Evans () and Susan Halabi ()
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Tomoyuki Sugimoto: Shiga University
Toshimitsu Hamasaki: National Cerebral and Cardiovascular Center
Scott R. Evans: George Washington University
Susan Halabi: Duke University School of Medicine

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2020, vol. 26, issue 2, No 3, 266-291

Abstract: 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.

Keywords: Bivariate dependence; Error-spending method; Independent censoring; Logrank statistic; Non-fatal events; Normal approximation (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10985-019-09470-4

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