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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Weighted Lindley frailty model: estimation and application to lung cancer data

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Author(s):
Mota, Alex [1, 2, 3] ; Milani, Eder A. [3] ; Calsavara, Vinicius F. [4, 5] ; Tomazella, Vera L. D. [2] ; Leao, Jeremias [6] ; Ramos, Pedro L. [7] ; Ferreira, Paulo H. [8] ; Louzada, Francisco [1]
Total Authors: 8
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP - Brazil
[2] Univ Fed Sao Carlos, Dept Stat, BR-13565905 Sao Carlos, SP - Brazil
[3] Univ Fed Goias, Inst Math & Stat, BR-74690900 Goiania, Go - Brazil
[4] AC Camargo Canc Ctr, Dept Epidemiol & Stat, BR-01508010 Sao Paulo, SP - Brazil
[5] Cedars Sinai Med Ctr, Biostat & Bioinformat Res Ctr, Los Angeles, CA 90048 - USA
[6] Univ Fed Amazonas, Dept Stat, BR-69067005 Manaus, Amazonas - Brazil
[7] Pontificia Univ Catolica Chile, Fac Matemat, Santiago 7820436 - Chile
[8] Univ Fed Bahia, Dept Stat, BR-40170110 Salvador, BA - Brazil
Total Affiliations: 8
Document type: Journal article
Source: LIFETIME DATA ANALYSIS; JUL 2021.
Web of Science Citations: 0
Abstract

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. (AU)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC