Transmuted Cure Rate Models - A Competing Risk Perspective
DOI:
https://doi.org/10.60923/issn.1973-2201/21223Keywords:
Bootstrap, Cure rate, Metropolis-Hastings algorithm, Transmuted distributionsAbstract
A cure rate model under a competing risk scenario where the number of competing causes follow a shifted binomial distribution with parameter p is proposed. Interestingly, the resulting distribution is the well-studied transmuted class of distribution. Few existing cure rate models are shown to be special cases of the proposed model. The identifiability issues of the model are studied in detail. Further properties of the model are investigated, and we discuss the maximum likelihood estimation of the parameter. The performance is confirmed through a simulation study using a defective Gompertz baseline and with competing causes. The Bayesian approach to the estimation of the parameter is adopted. The complexity of the likelihood function is handled through the Metropolis-Hastings algorithm. We analyse the data consisting of 8966 patients who have undergone bone marrow transplantation at the European Society for Blood and Marrow Transplantation (EBMT). The validation of the estimation algorithm is conformed using the bootstrap technique.
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