A survival regression with cure fraction applied to cervical cancer

A new survival model is proposed in the presence of surviving fractions and unobserved dispersion. It is obtained by considering several latent factors (or risks) that generated the observed lifetime which follows a generalized Poisson distribution, and it includes as a special case, the promotion time cure model. We explore maximum likelihood tools for inference issues by aid of the expectation maximization algorithm for estimating the parameters while model discrimination problem is treated by the aid of the likelihood ratio test. The new regression is applied to cervical cancer data to evaluate covariates effects in the cured fraction and non-cured group.

Automated summary

A new survival model is proposed in this paper to consider the presence of surviving fractions and unobserved dispersion. The model is obtained by considering multiple latent factors that generate the observed lifetime according to a generalized Poisson distribution, including the promotion time cure model as a special case. Maximum likelihood tools and the expectation maximization algorithm are used for inference and parameter estimation, while the likelihood ratio test is employed for model discrimination. The new regression model is applied to cervical cancer data to evaluate the effects of covariates on the cured fraction and non-cured group.