The standard nonparametric, semi-parametric, or parametric models were commonly used for a time to event data analysis. The other common situation in the analysis of lifetime data is a cure rate model in which it is assumed that a significant proportion of patients will never experience the event of interest. In cure models, it is assumed that the studied population is a mixture of individuals who experience the event of interest (susceptible) and individuals who will never experience the event (non-susceptible). The presence of cured individuals in a data set is usually suggested by the Kaplan-Meier plot of the survival function which shows a long and emergency plateau in the plot. The cure rate models are usually used to model this type of data. In this paper, the comparison is made by using the models to describe the oral cancer data and it is found that mixture cure models with exponentiated Weibull distribution provide a better fit to the oral lifetime data. Also, parametric mixture cure models fit better than the non-mixture cure models with respect to deviance information criterion (DIC).

survival analysis, cure rate estimation, parametric distributions, oral cancer data, MCMC algorithm.

Received: August 27, 2021; Accepted: October 6, 2021; Published:  October 11, 2021

How to cite this article: T. Bindu, M. Kumaran and T. P. Sajith Babu, Comparison of mixture and non-mixture cure models with standard parametric distributions: application to oral cancer data, JP Journal of Biostatistics 18(3) (2021), 409-427. DOI: 10.17654/BS018010409

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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