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Tests for comparing estimated survival functions

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  • C. Chauvel
  • J. O'Quigley

Abstract

We describe a class of statistical tests for the comparison of two or more survival curves, typically estimated using the Kaplan–Meier method. The class is based on the construction of O’Quigley (2003), and some special cases are of particular interest. Underlying the inferential development are the arguments of Efron & Hinkley (1978), leading to a theoretical sampling model that is in some sense closer to the observed data. The log-rank and weighted log-rank tests arise as special members of the class. In practice the log-rank test will often be a suboptimal, even poor, test due to the presence of non-proportional hazards. The proposed test maintains good power and, in all the cases considered, has greater power than the log-rank test under non-proportional hazards. The power will depend on the alternatives being considered, and under reasonable assumptions on the alternatives, we conclude that the proposed test is more powerful than the log-rank test. Simulations support these conclusions. An example is given as an illustration.

Suggested Citation

  • C. Chauvel & J. O'Quigley, 2014. "Tests for comparing estimated survival functions," Biometrika, Biometrika Trust, vol. 101(3), pages 535-552.
    • Handle: RePEc:oup:biomet:v:101:y:2014:i:3:p:535-552.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu015
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    Cited by:

    1. Grzegorz Wyłupek, 2021. "A permutation test for the two-sample right-censored model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(5), pages 1037-1061, October.
    2. Cécile Chauvel & John O'Quigley, 2017. "Survival model construction guided by fit and predictive strength," Biometrics, The International Biometric Society, vol. 73(2), pages 483-494, June.

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