A 3-parameter Gompertz distribution for survival data with competing risks, with an application to breast cancer data

S. R. Haile, J.-H. Jeong, X. Chen and Y. Cheng

Journal of Applied Statistics, 2016, vol. 43, issue 12, 2239-2253

Abstract: The cumulative incidence function is of great importance in the analysis of survival data when competing risks are present. Parametric modeling of such functions, which are by nature improper, suggests the use of improper distributions. One frequently used improper distribution is that of Gompertz, which captures only monotone hazard shapes. In some applications, however, subdistribution hazard estimates have been observed with unimodal shapes. An extension to the Gompertz distribution is presented which can capture unimodal as well as monotone hazard shapes. Important properties of the proposed distribution are discussed, and the proposed distribution is used to analyze survival data from a breast cancer clinical trial.

Date: 2016
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DOI: 10.1080/02664763.2015.1134450

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