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Articles

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

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Pages 2239-2253 | Received 30 Jul 2015, Accepted 16 Dec 2015, Published online: 21 Jan 2016
 

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.

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Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research was partly supported by the National Health Institute [grant numbers 5-U10-CA69974-09, 5-U10-CA69651-11 to Jeong], Swiss National Science Foundation [grant numbers 122668, 122675 to Haile] and National Science Foundation [grant DMS-1207711 to Cheng].

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