Likelihood inference for COM-Poisson cure rate model with interval-censored data and Weibull lifetimes. (October 2017)

• Record Type: Journal Article
• Authors: Pal, Suvra; Balakrishnan, N
• Other Names: Balakrishnan N. guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 26:Number 5(2017)
• Page Start: 2093
• Page End: 2113
• Publication Date: 2017-10
• Subjects: Long-term survival model -- maximum likelihood estimate -- expectation maximization algorithm -- profile likelihood -- likelihood ratio test -- Akaike information criterion -- Bayesian information criterion; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217708686
• Languages: English
• ISSNs: 0962-2802

Abstract

In this paper, we consider a competing cause scenario and assume the number of competing causes to follow a Conway–Maxwell Poisson distribution which can capture both over and under dispersion that is usually encountered in discrete data. Assuming the population of interest having a component cure and the form of the data to be interval censored, as opposed to the usually considered right-censored data, the main contribution is in developing the steps of the expectation maximization algorithm for the determination of the maximum likelihood estimates of the model parameters of the flexible Conway–Maxwell Poisson cure rate model with Weibull lifetimes. An extensive Monte Carlo simulation study is carried out to demonstrate the performance of the proposed estimation method. Model discrimination within the Conway–Maxwell Poisson distribution is addressed using the likelihood ratio test and information-based criteria to select a suitable competing cause distribution that provides the best fit to the data. A simulation study is also carried out to demonstrate the loss in efficiency when selecting an improper competing cause distribution which justifies the use of a flexible family of distributions for the number of competing causes. Finally, the proposed methodology and the flexibility of the Conway–Maxwell Poisson distribution are illustrated with two known data sets from the literature: smoking cessation data and breast cosmesis data.