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Optimality of testing procedures for survival data in the nonproportional hazards setting

Author

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  • Andrea Arfè
  • Brian Alexander
  • Lorenzo Trippa

Abstract

Most statistical tests for treatment effects used in randomized clinical trials with survival outcomes are based on the proportional hazards assumption, which often fails in practice. Data from early exploratory studies may provide evidence of nonproportional hazards, which can guide the choice of alternative tests in the design of practice‐changing confirmatory trials. We developed a test to detect treatment effects in a late‐stage trial, which accounts for the deviations from proportional hazards suggested by early‐stage data. Conditional on early‐stage data, among all tests that control the frequentist Type I error rate at a fixed α level, our testing procedure maximizes the Bayesian predictive probability that the study will demonstrate the efficacy of the experimental treatment. Hence, the proposed test provides a useful benchmark for other tests commonly used in the presence of nonproportional hazards, for example, weighted log‐rank tests. We illustrate this approach in simulations based on data from a published cancer immunotherapy phase III trial.

Suggested Citation

  • Andrea Arfè & Brian Alexander & Lorenzo Trippa, 2021. "Optimality of testing procedures for survival data in the nonproportional hazards setting," Biometrics, The International Biometric Society, vol. 77(2), pages 587-598, June.
    • Handle: RePEc:bla:biomet:v:77:y:2021:i:2:p:587-598
    DOI: 10.1111/biom.13315
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    References listed on IDEAS

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    1. Song Yang & Ross Prentice, 2010. "Improved Logrank-Type Tests for Survival Data Using Adaptive Weights," Biometrics, The International Biometric Society, vol. 66(1), pages 30-38, March.
    2. Brian P. Hobbs & Bradley P. Carlin & Sumithra J. Mandrekar & Daniel J. Sargent, 2011. "Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials," Biometrics, The International Biometric Society, vol. 67(3), pages 1047-1056, September.
    3. Steffen Ventz & Lorenzo Trippa, 2015. "Bayesian designs and the control of frequentist characteristics: A practical solution," Biometrics, The International Biometric Society, vol. 71(1), pages 218-226, March.
    4. Satoshi Morita & Peter F. Thall & Peter Müller, 2008. "Determining the Effective Sample Size of a Parametric Prior," Biometrics, The International Biometric Society, vol. 64(2), pages 595-602, June.
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