Journal
COMPUTATIONAL STATISTICS
Volume 30, Issue 1, Pages 151-189Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s00180-014-0527-9
Keywords
Long-term survivor; Conway-Maxwell Poisson (COM-Poisson) distribution; Maximum likelihood estimators; Likelihood ratio test; Akaike information criterion (AIC); Bayesian information criterion (BIC)
Categories
Funding
- Natural Sciences and Engineering Research Council of Canada
Ask authors/readers for more resources
In this paper, we consider the Conway-Maxwell Poisson (COM-Poisson) cure rate model based on a competing risks scenario. This model includes, as special cases, some of the well-known cure rate models discussed in the literature. By assuming the time-to-event to follow the generalized gamma distribution, which contains some of the commonly used lifetime distributions as special cases, we develop exact likelihood inference based on the expectation maximization algorithm. The standard errors of the maximum likelihood estimates are obtained by inverting the observed information matrix. An extensive Monte Carlo simulation study is performed to examine the method of inference developed here. Model discrimination within the generalized gamma family is also carried out by means of likelihood- and information-based methods to select the particular lifetime distribution that provides an adequate fit to the data. Finally, a data on cancer recurrence is analyzed to illustrate the flexibility of the COM-Poisson family and the generalized gamma family so as to select a parsimonious competing cause distribution and a lifetime distribution that jointly provide an adequate fit to the data.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available