Literature DB >> 11318168

A group sequential test for survival trials: an alternative to rank-based procedures.

Z Li1.   

Abstract

A method of interim monitoring is described for survival trials in which the proportional hazards assumption may not hold. This method extends the test statistics based on the cumulative weighted difference in the Kaplan-Meier estimates (Pepe and Fleming, 1989, Biometrics 45, 497-507) to the sequential setting. Therefore, it provides a useful alternative to the group sequential linear rank tests. With an appropriate weight function, the test statistic itself provides an estimator for the cumulative weighted difference in survival probabilities, which is an interpretable measure for the treatment difference, especially when the proportional hazards model fails. The method is illustrated based on the design of a real trial. The operating characteristics are studied through a small simulation.

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Year:  1999        PMID: 11318168     DOI: 10.1111/j.0006-341x.1999.00277.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  4 in total

1.  A computationally simple bivariate survival estimator for efficacy and safety.

Authors:  Denise Scholtens; Rebecca A Betensky
Journal:  Lifetime Data Anal       Date:  2006-08-18       Impact factor: 1.588

2.  Sequential tests for non-proportional hazards data.

Authors:  Matthias Brückner; Werner Brannath
Journal:  Lifetime Data Anal       Date:  2016-03-11       Impact factor: 1.588

3.  Utilizing the integrated difference of two survival functions to quantify the treatment contrast for designing, monitoring, and analyzing a comparative clinical study.

Authors:  Lihui Zhao; Lu Tian; Hajime Uno; Scott D Solomon; Marc A Pfeffer; Jerald S Schindler; Lee Jen Wei
Journal:  Clin Trials       Date:  2012-08-22       Impact factor: 2.486

Review 4.  Group sequential tests for long-term survival comparisons.

Authors:  Brent R Logan; Shuyuan Mo
Journal:  Lifetime Data Anal       Date:  2014-07-23       Impact factor: 1.588

  4 in total

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