 The generalized exponential cure rate model with covariatesNandini Kannan, Debasis Kundu, P. Nair and R. C. Tripathi Journal of Applied Statistics, 2010, vol. 37, issue 10, 1625-1636 Abstract: In this article, we consider a parametric survival model that is appropriate when the population of interest contains long-term survivors or immunes. The model referred to as the cure rate model was introduced by Boag 1 in terms of a mixture model that included a component representing the proportion of immunes and a distribution representing the life times of the susceptible population. We propose a cure rate model based on the generalized exponential distribution that incorporates the effects of risk factors or covariates on the probability of an individual being a long-time survivor. Maximum likelihood estimators of the model parameters are obtained using the the expectation-maximisation (EM) algorithm. A graphical method is also provided for assessing the goodness-of-fit of the model. We present an example to illustrate the fit of this model to data that examines the effects of different risk factors on relapse time for drug addicts. Keywords: cure rate; long-term survivor; generalized exponential distribution; EM algorithm; goodness-of-fit (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:37:y:2010:i:10:p:1625-1636 Ordering information: This journal article can be ordered from DOI: 10.1080/02664760903117739 Access Statistics for this article Journal of Applied Statistics is currently edited by Robert Aykroyd More articles in Journal of Applied Statistics from Taylor & Francis Journals |
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