Weighted Lindley frailty model: estimation and application to lung cancer data
| Autor: | Francisco Louzada, Paulo H. Ferreira, Vinicius F. Calsavara, Eder Angelo Milani, Jeremias Leão, Vera Tomazella, Alex L. Mota, Pedro L. Ramos |
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| Rok vydání: | 2021 |
| Předmět: |
Hazard (logic)
education.field_of_study Likelihood Functions Lung Neoplasms Frailty VEROSSIMILHANÇA Applied Mathematics Gompertz function Population Inference Estimator General Medicine Survival Analysis Sample size determination Likelihood-ratio test Statistics Statistics::Methodology Humans education Brazil Weibull distribution Mathematics Proportional Hazards Models |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| Popis: | In this paper, we propose a novel frailty model for modeling unobserved heterogeneity present in survival data. Our model is derived by using a weighted Lindley distribution as the frailty distribution. The respective frailty distribution has a simple Laplace transform function which is useful to obtain marginal survival and hazard functions. We assume hazard functions of the Weibull and Gompertz distributions as the baseline hazard functions. A classical inference procedure based on the maximum likelihood method is presented. Extensive simulation studies are further performed to verify the behavior of maximum likelihood estimators under different proportions of right-censoring and to assess the performance of the likelihood ratio test to detect unobserved heterogeneity in different sample sizes. Finally, to demonstrate the applicability of the proposed model, we use it to analyze a medical dataset from a population-based study of incident cases of lung cancer diagnosed in the state of Sao Paulo, Brazil. |
| Databáze: | OpenAIRE |
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