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A class of tests of proportional hazards assumption for left-truncated and right-censored data

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  • Wei Chen
  • Dehui Wang
  • Yanfeng Li

Abstract

In this paper, we proposed a class of tests of proportional hazards assumption for left-truncated and right-censored data based on a pair of estimators of the hazard ratio constant. Using counting process and martingale theory, the asymptotically normal distribution of the test statistic is derived and a family of consistent estimators of variance are also provided. Extensive simulation studies were conducted to evaluate the performance of the proposed test statistics under finite sample situations. Two real data sets are analyzed to illustrate our method.

Suggested Citation

  • Wei Chen & Dehui Wang & Yanfeng Li, 2015. "A class of tests of proportional hazards assumption for left-truncated and right-censored data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(11), pages 2307-2320, November.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2307-2320
    DOI: 10.1080/02664763.2015.1027884
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    Cited by:

    1. Tobias Bluhmki & Dennis Dobler & Jan Beyersmann & Markus Pauly, 2019. "The wild bootstrap for multivariate Nelson–Aalen estimators," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 97-127, January.
    2. Marc Ditzhaus & Arnold Janssen, 2020. "Bootstrap and permutation rank tests for proportional hazards under right censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 493-517, July.

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