A survival regression with cure fraction applied to cervical cancer

Vicente G. Cancho, Elizbeth C. Bedia, Gauss M. Cordeiro, Fábio Prataviera, Edwin M. M. Ortega () and Ana P. J. E. Santo
Additional contact information
Vicente G. Cancho: ICMC/USP
Elizbeth C. Bedia: ICMC/USP
Gauss M. Cordeiro: UFPE
Fábio Prataviera: ESALQ/USP
Edwin M. M. Ortega: ESALQ/USP
Ana P. J. E. Santo: ICMC/USP

Computational Statistics, 2023, vol. 38, issue 1, No 19, 403-418

Abstract: Abstract 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.

Keywords: Cure rate model; EM algorithm; Generalized Poisson distribution; Proportional hazards (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00180-022-01233-4

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