Literature DB >> 24347247

Methods of designing two-stage winner trials with survival outcomes.

Fang Fang1, Yong Lin, Weichung J Shih, Yulin Li, Jay Yang, Xiaosha Zhang.   

Abstract

In drug development, especially for oncology studies, a recent proposal is to combine a costly phase II dose selection study with a subsequent phase III study into a single trial that compares the selected (winning) dose from the first stage with the control group. This design may also be used in phase III trials, in which the winning active treatment regimen, selected at the first stage, is compared with the control group at the second stage. This design is known as a two-stage winner design, as proposed by Shun et al. (2008) for continuous outcomes. Time-to-event data are often analyzed in oncology trials. In order to derive the critical value and power of this design, per Shun et al. (2008), it is essential to calculate the asymptotic covariance and correlation of the log-rank statistics for survival outcomes between the two stages. In this paper, we derive the asymptotic covariance and correlation, and provide additional approximate design parameters. Examples are given to illustrate the method, and simulations are performed to evaluate the veracity of these approximate design parameters.
Copyright © 2013 John Wiley & Sons, Ltd.

Entities:  

Keywords:  asymptotic correlation; log-rank statistic; survival outcome; two-stage winner design

Mesh:

Year:  2013        PMID: 24347247      PMCID: PMC3976764          DOI: 10.1002/sim.6070

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  4 in total

1.  Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer.

Authors:  Patrick Royston; Mahesh K B Parmar; Wendi Qian
Journal:  Stat Med       Date:  2003-07-30       Impact factor: 2.373

2.  Planning and analyzing adaptive group sequential survival trials.

Authors:  Gernot Wassmer
Journal:  Biom J       Date:  2006-08       Impact factor: 2.207

3.  Interim treatment selection using the normal approximation approach in clinical trials.

Authors:  Zhenming Shun; K K Gordon Lan; Yuhwen Soo
Journal:  Stat Med       Date:  2008-02-20       Impact factor: 2.373

4.  Power of logrank test and Cox regression model in clinical trials with heterogeneous samples.

Authors:  K Akazawa; T Nakamura; Y Palesch
Journal:  Stat Med       Date:  1997-03-15       Impact factor: 2.373

  4 in total

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