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Sample size formulae for two-stage randomized trials with survival outcomes

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  • Zhiguo Li
  • Susan A. Murphy

Abstract

Two-stage randomized trials are growing in importance in developing adaptive treatment strategies, i.e. treatment policies or dynamic treatment regimes. Usually, the first stage involves randomization to one of the several initial treatments. The second stage of treatment begins when an early nonresponse criterion or response criterion is met. In the second-stage, nonresponding subjects are re-randomized among second-stage treatments. Sample size calculations for planning these two-stage randomized trials with failure time outcomes are challenging because the variances of common test statistics depend in a complex manner on the joint distribution of time to the early nonresponse criterion or response criterion and the primary failure time outcome. We produce simple, albeit conservative, sample size formulae by using upper bounds on the variances. The resulting formulae only require the working assumptions needed to size a standard single-stage randomized trial and, in common settings, are only mildly conservative. These sample size formulae are based on either a weighted Kaplan--Meier estimator of survival probabilities at a fixed time-point or a weighted version of the log-rank test. Copyright 2011, Oxford University Press.

Suggested Citation

  • Zhiguo Li & Susan A. Murphy, 2011. "Sample size formulae for two-stage randomized trials with survival outcomes," Biometrika, Biometrika Trust, vol. 98(3), pages 503-518.
  • Handle: RePEc:oup:biomet:v:98:y:2011:i:3:p:503-518
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    File URL: http://hdl.handle.net/10.1093/biomet/asr019
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    Cited by:

    1. Zhiwei Jiang & Ling Wang & Chanjuan Li & Jielai Xia & Hongxia Jia, 2012. "A Practical Simulation Method to Calculate Sample Size of Group Sequential Trials for Time-to-Event Data under Exponential and Weibull Distribution," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-12, September.

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