Proportional hazards under Conway–Maxwell-Poisson cure rate model and associated inference. (October 2017)

• Record Type: Journal Article
• Authors: Balakrishnan, N; Barui, S; Milienos, FS
• Other Names: Balakrishnan N. guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 26:Number 5(2017)
• Page Start: 2055
• Page End: 2077
• Publication Date: 2017-10
• Subjects: Expectation-maximization algorithm -- model discrimination -- Akaike's information criterion -- Bayesian information criterion -- cutaneous melanoma data; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217708683
• Languages: English
• ISSNs: 0962-2802

Abstract

Cure rate models or long-term survival models play an important role in survival analysis and some other applied fields. In this article, by assuming a Conway–Maxwell–Poisson distribution under a competing cause scenario, we study a flexible cure rate model in which the lifetimes of non-cured individuals are described by a Cox's proportional hazard model with a Weibull hazard as the baseline function. Inference is then developed for a right censored data by the maximum likelihood method with the use of expectation-maximization algorithm and a profile likelihood approach for the estimation of the dispersion parameter of the Conway–Maxwell–Poisson distribution. An extensive simulation study is performed, under different scenarios including various censoring proportions, sample sizes, and lifetime parameters, in order to evaluate the performance of the proposed inferential method. Discrimination among some common cure rate models is then done by using likelihood-based and information-based criteria. Finally, for illustrative purpose, the proposed model and associated inferential procedure are applied to analyze a cutaneous melanoma data.