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

Tomoyuki Sugimoto | Zendy; Toshimitsu Hamasaki | Zendy; Scott Evans | Zendy; Susan Halabi | Zendy

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

Author(s) -

Tomoyuki Sugimoto,

Toshimitsu Hamasaki,

Scott Evans,

Susan Halabi

Publication year - 2019

Publication title -

lifetime data analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.677

H-Index - 46

eISSN - 1572-9249

pISSN - 1380-7870

DOI - 10.1007/s10985-019-09470-4

Subject(s) - statistics , bivariate analysis , event (particle physics) , mathematics , random variate , log rank test , statistic , sample size determination , early stopping , joint probability distribution , computer science , survival analysis , artificial intelligence , random variable , physics , quantum mechanics , artificial neural network

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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