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Proportional Hazards Changepoint Models in Survival Analysis

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  • A. A. Noura
  • K. L. Q. Read

Abstract

This paper describes an approach to proportional hazards analysis of survival data with covariates by parametric modelling of the base‐line hazard in terms of piecewise distributions. Maximum likelihood estimation using GLIM and an iterative method is straightforward. Applications of the method and its use with competing risks are given, in which a two‐piece Weibull fit is clearly superior to the simple Weibull model. Wide generality results from the fact that any given montonic increasing transformation may be applied to the base‐line hazard parameter. It can be expected that piecewise models of this kind will usefully describe many proportional hazards survival processes involving changepoints at which the ruling conditions suddenly alter.

Suggested Citation

  • A. A. Noura & K. L. Q. Read, 1990. "Proportional Hazards Changepoint Models in Survival Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 39(2), pages 241-253, June.
    • Handle: RePEc:bla:jorssc:v:39:y:1990:i:2:p:241-253
    DOI: 10.2307/2347763
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    Cited by:

    1. Vito Muggeo & Massimo Attanasio & Mariano Porcu, 2009. "A segmented regression model for event history data: an application to the fertility patterns in Italy," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(9), pages 973-988.
    2. Alessandra R. Brazzale & Helmut Küchenhoff & Stefanie Krügel & Tobias S. Schiergens & Heiko Trentzsch & Wolfgang Hartl, 2019. "Nonparametric change point estimation for survival distributions with a partially constant hazard rate," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 301-321, April.

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