EM Algorithm-Based Likelihood Estimation for Some Cure Rate Models

In the recent work of Rodrigues et al. (2009) , a flexible cure rate survival model was developed by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell Poisson distribution. This model includes as special cases some of the well-known cure rate models discus...

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Bibliographic Details
Published inJournal of statistical theory and practice Vol. 6; no. 4; pp. 698 - 724
Main Authors Balakrishnan, N., Pal, S.
Format Journal Article
LanguageEnglish
Published Cham Taylor & Francis Group 01.12.2012
Springer International Publishing
Subjects
Asymptotic variances
Conway-Maxwell Poisson (COM-Poisson) distribution
Cure rate models
EM algorithm
Exponential distribution
Lifetime data
Mathematics and Statistics
Maximum likelihood estimators
Probability Theory and Stochastic Processes
Profile likelihood
Statistical Theory and Methods
Statistics
Profile likelihood
62P10
Conway-Maxwell Poisson (COM-Poisson) distribution
Exponential distribution
62N02
Maximum likelihood estimators
Cure rate models
Lifetime data
EM algorithm
Asymptotic variances
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Summary:In the recent work of Rodrigues et al. (2009) , a flexible cure rate survival model was developed by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell Poisson distribution. This model includes as special cases some of the well-known cure rate models discussed in the literature. As the data obtained from cancer clinical trials are often subject to right censoring, the expectation maximization (EM) algorithm can be used as a powerful and efficient tool for the estimation of the model parameters based on right censored data. In this paper, the cure rate model developed by Rodrigues et al. (2009) is considered and assuming the time-to-event to follow the exponential distribution, exact likelihood inference is developed based on the EM algorithm. The inverse of the observed information matrix is used to compute the standard errors of the maximum likelihood estimates (MLEs). An extensive Monte Carlo simulation study is performed to illustrate the method of inference developed here. Finally, the proposed methodology is illustrated with real data on cutaneous melanoma.
ISSN:1559-8608
1559-8616
DOI:10.1080/15598608.2012.719803