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Combined multiple testing of multivariate survival times by censored empirical likelihood

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  • Judith H. Parkinson

Abstract

In each study testing the survival experience of one or more populations, one must not only choose an appropriate class of tests, but further an appropriate weight function. As the optimal choice depends on the true shape of the hazard ratio, one is often not capable of getting the best results with respect to a specific dataset. For the univariate case several methods were proposed to conquer this problem. However, most of the interesting datasets contain multivariate observations nowadays. In this work we propose a multivariate version of a method based on multiple constrained censored empirical likelihood where the constraints are formulated as linear functionals of the cumulative hazard functions. By considering the conditional hazards, we take the correlation between the components into account with the goal of obtaining a test that exhibits a high power irrespective of the shape of the hazard ratio under the alternative hypothesis.

Suggested Citation

  • Judith H. Parkinson, 2020. "Combined multiple testing of multivariate survival times by censored empirical likelihood," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 757-786, September.
  • Handle: RePEc:bla:scjsta:v:47:y:2020:i:3:p:757-786
    DOI: 10.1111/sjos.12423
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    References listed on IDEAS

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    1. Michael Brendel & Arnold Janssen & Claus-Dieter Mayer & Markus Pauly, 2014. "Weighted Logrank Permutation Tests for Randomly Right Censored Life Science Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 742-761, September.
    2. Pan, Xiao-Rong & Zhou, Mai, 2002. "Empirical Likelihood Ratio in Terms of Cumulative Hazard Function for Censored Data," Journal of Multivariate Analysis, Elsevier, vol. 80(1), pages 166-188, January.
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