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Nonparametric inference for assessing treatment efficacy in randomized clinical trials with a time-to-event outcome and all-or-none compliance

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  • Robert M. Elashoff
  • Gang Li
  • Ying Zhou

Abstract

To evaluate the biological efficacy of a treatment in a randomized clinical trial, one needs to compare patients in the treatment arm who actually received treatment with the subgroup of patients in the control arm who would have received treatment had they been randomized into the treatment arm. In practice, subgroup membership in the control arm is usually unobservable. This paper develops a nonparametric inference procedure to compare subgroup probabilities with right-censored time-to-event data and unobservable subgroup membership in the control arm. We also present a procedure to estimate the onset and duration of treatment effect. The performance of our method is evaluated by simulation. An illustration is given using a randomized clinical trial for melanoma. Copyright 2012, Oxford University Press.

Suggested Citation

  • Robert M. Elashoff & Gang Li & Ying Zhou, 2012. "Nonparametric inference for assessing treatment efficacy in randomized clinical trials with a time-to-event outcome and all-or-none compliance," Biometrika, Biometrika Trust, vol. 99(2), pages 393-404.
    • Handle: RePEc:oup:biomet:v:99:y:2012:i:2:p:393-404
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    File URL: http://hdl.handle.net/10.1093/biomet/ass004
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    Cited by:

    1. L. Altstein & G. Li, 2013. "Latent Subgroup Analysis of a Randomized Clinical Trial through a Semiparametric Accelerated Failure Time Mixture Model," Biometrics, The International Biometric Society, vol. 69(1), pages 52-61, March.

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