Expectation maximization-based likelihood inference for flexible cure rate models with Weibull lifetimes. (August 2016)

• Record Type: Journal Article
• Authors: Balakrishnan, Narayanaswamy; Pal, Suvra
• Other Names: Lawson Andrew B guest-editor.; Lee Duncan guest-editor.; MacNab Ying guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 25:Number 4(2016:Aug.)
• Page Start: 1535
• Page End: 1563
• Publication Date: 2016-08
• Subjects: cure rate models -- long-term survivor -- Conway–Maxwell–Poisson distribution -- maximum likelihood estimators -- expectation maximization algorithm -- profile likelihood -- lifetime data -- Weibull distribution -- asymptotic variances -- Akaike's 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: 10.1177/0962280213491641
• Languages: English
• ISSNs: 0962-2802

Abstract

Recently, a flexible cure rate survival model has been developed by assuming the number of competing causes of the event of interest to follow the Conway–Maxwell–Poisson distribution. This model includes some of the well-known cure rate models discussed in the literature as special cases. Data obtained from cancer clinical trials are often right censored and expectation maximization algorithm can be used in this case to efficiently estimate the model parameters based on right censored data. In this paper, we consider the competing cause scenario and assuming the time-to-event to follow the Weibull distribution, we derive the necessary steps of the expectation maximization algorithm for estimating the parameters of different cure rate survival models. The standard errors of the maximum likelihood estimates are obtained by inverting the observed information matrix. The method of inference developed here is examined by means of an extensive Monte Carlo simulation study. Finally, we illustrate the proposed methodology with a real data on cancer recurrence.