EM algorithm-based likelihood estimation for a generalized Gompertz regression model in presence of survival data with long-term survivors: an application to uterine cervical cancer data. Issue 9 (13th June 2017)

• Record Type: Journal Article
• Authors: Borges, Patrick
• Is Part Of: Journal of statistical computation and simulation. Volume 87:Issue 9(2017)
• Page Start: 1712
• Page End: 1722
• Publication Date: 2017-06-13
• Subjects: Generalized Gompertz distribution -- defective distributions -- cure fraction -- regression model -- EM algorithm -- bootstrap method; 62N01 -- 62N02 -- 62E15 -- 62E20; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2017.1281927
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: In this paper we develop a regression model for survival data in the presence of long-term survivors based on the generalized Gompertz distribution introduced by El-Gohary et al. [The generalized Gompertz distribution. Appl Math Model. 2013;37:13–24] in a defective version. This model includes as special case the Gompertz cure rate model proposed by Gieser et al. [Modelling cure rates using the Gompertz model with covariate information. Stat Med. 1998;17:831–839]. Next, an expectation maximization algorithm is then developed for determining the maximum likelihood estimates (MLEs) of the parameters of the model. In addition, we discuss the construction of confidence intervals for the parameters using the asymptotic distributions of the MLEs and the parametric bootstrap method, and assess their performance through a Monte Carlo simulation study. Finally, the proposed methodology was applied to a database on uterine cervical cancer.