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Flexible parametric modelling of the hazard function in breast cancer studies

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  • I. Ardoino
  • E. M. Biganzoli
  • C. Bajdik
  • P. J. Lisboa
  • P. Boracchi
  • F. Ambrogi

Abstract

In cancer research, study of the hazard function provides useful insights into disease dynamics, as it describes the way in which the (conditional) probability of death changes with time. The widely utilized Cox proportional hazard model uses a stepwise nonparametric estimator for the baseline hazard function, and therefore has a limited utility. The use of parametric models and/or other approaches that enables direct estimation of the hazard function is often invoked. A recent work by Cox et al . [6] has stimulated the use of the flexible parametric model based on the Generalized Gamma (GG) distribution, supported by the development of optimization software. The GG distribution allows estimation of different hazard shapes in a single framework. We use the GG model to investigate the shape of the hazard function in early breast cancer patients. The flexible approach based on a piecewise exponential model and the nonparametric additive hazards model are also considered.

Suggested Citation

  • I. Ardoino & E. M. Biganzoli & C. Bajdik & P. J. Lisboa & P. Boracchi & F. Ambrogi, 2012. "Flexible parametric modelling of the hazard function in breast cancer studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1409-1421, December.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1409-1421
    DOI: 10.1080/02664763.2011.650685
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    References listed on IDEAS

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    1. Boracchi, Patrizia & Biganzoli, Elia & Marubini, Ettore, 2003. "Joint modelling of cause-specific hazard functions with cubic splines: an application to a large series of breast cancer patients," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 243-262, February.
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