Defective models induced by gamma frailty term for survival data with cured fraction

Juliana Scudilio, Vinicius F. Calsavara, Ricardo Rocha, Francisco Louzada, Vera Tomazella and Agatha S. Rodrigues

Journal of Applied Statistics, 2019, vol. 46, issue 3, 484-507

Abstract: In this paper, we propose a defective model induced by a frailty term for modeling the proportion of cured. Unlike most of the cure rate models, defective models have advantage of modeling the cure rate without adding any extra parameter in model. The introduction of an unobserved heterogeneity among individuals has bring advantages for the estimated model. The influence of unobserved covariates is incorporated using a proportional hazard model. The frailty term assumed to follow a gamma distribution is introduced on the hazard rate to control the unobservable heterogeneity of the patients. We assume that the baseline distribution follows a Gompertz and inverse Gaussian defective distributions. Thus we propose and discuss two defective distributions: the defective gamma-Gompertz and gamma-inverse Gaussian regression models. Simulation studies are performed to verify the asymptotic properties of the maximum likelihood estimator. Lastly, in order to illustrate the proposed model, we present three applications in real data sets, in which one of them we are using for the first time, related to a study about breast cancer in the A.C.Camargo Cancer Center, São Paulo, Brazil.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:46:y:2019:i:3:p:484-507

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DOI: 10.1080/02664763.2018.1498464

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