Literature DB >> 30968271

A new long-term survival model with dispersion induced by discrete frailty.

Vicente G Cancho1, Márcia A C Macera2, Adriano K Suzuki1, Francisco Louzada1, Katherine E C Zavaleta3.   

Abstract

Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there are situations where a discretely-distributed frailty may be appropriate. In this paper, we propose extending the proportional hazards frailty models to allow a discrete distribution for the frailty variable. Having zero frailty can be interpreted as being immune or cured (long-term survivors). Thus, we develop a new survival model induced by discrete frailty with zero-inflated power series distribution, which can account for overdispersion. A numerical study is carried out under the scenario that the baseline distribution follows an exponential distribution, however this assumption can be easily relaxed and some other distributions can be considered. Moreover, this proposal allows for a more realistic description of the non-risk individuals, since individuals cured due to intrinsic factors (immune) are modeled by a deterministic fraction of zero-risk while those cured due to an intervention are modeled by a random fraction. Inference is developed by the maximum likelihood method for the estimation of the model parameters. A simulation study is performed in order to evaluate the performance of the proposed inferential method. Finally, the proposed model is applied to a data set on malignant cutaneous melanoma to illustrate the methodology.

Entities:  

Keywords:  Cure rate models; Discrete frailty; Maximum likelihood estimation; Overdispersion; Zero-inflated power series distribution

Mesh:

Year:  2019        PMID: 30968271     DOI: 10.1007/s10985-019-09472-2

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  10 in total

1.  Multivariate survival models induced by genetic frailties, with application to linkage analysis.

Authors:  Hongzhe Li; Xiaoyun Zhong
Journal:  Biostatistics       Date:  2002-03       Impact factor: 5.899

2.  Proportional hazards models with discrete frailty.

Authors:  Chrys Caroni; Martin Crowder; Alan Kimber
Journal:  Lifetime Data Anal       Date:  2010-01-29       Impact factor: 1.588

3.  Estimating Cure Rates From Survival Data: An Alternative to Two-Component Mixture Models.

Authors:  A D Tsodikov; J G Ibrahim; A Y Yakovlev
Journal:  J Am Stat Assoc       Date:  2003-12-01       Impact factor: 5.033

4.  Regression analysis of discrete time survival data under heterogeneity.

Authors:  X Xue; R Brookmeyer
Journal:  Stat Med       Date:  1997-09-15       Impact factor: 2.373

5.  A power series beta Weibull regression model for predicting breast carcinoma.

Authors:  Edwin M M Ortega; Gauss M Cordeiro; Ana K Campelo; Michael W Kattan; Vicente G Cancho
Journal:  Stat Med       Date:  2015-01-26       Impact factor: 2.373

6.  The impact of heterogeneity in individual frailty on the dynamics of mortality.

Authors:  J W Vaupel; K G Manton; E Stallard
Journal:  Demography       Date:  1979-08

7.  A score test for zero inflation in a Poisson distribution.

Authors:  J van den Broek
Journal:  Biometrics       Date:  1995-06       Impact factor: 2.571

8.  High- and low-dose interferon alfa-2b in high-risk melanoma: first analysis of intergroup trial E1690/S9111/C9190.

Authors:  J M Kirkwood; J G Ibrahim; V K Sondak; J Richards; L E Flaherty; M S Ernstoff; T J Smith; U Rao; M Steele; R H Blum
Journal:  J Clin Oncol       Date:  2000-06       Impact factor: 44.544

9.  Marginal regression models for clustered count data based on zero-inflated Conway-Maxwell-Poisson distribution with applications.

Authors:  Hyoyoung Choo-Wosoba; Steven M Levy; Somnath Datta
Journal:  Biometrics       Date:  2015-11-17       Impact factor: 2.571

10.  Frailty modelling of testicular cancer incidence using Scandinavian data.

Authors:  Tron A Moger; Odd O Aalen; Tarje O Halvorsen; Hans H Storm; Steinar Tretli
Journal:  Biostatistics       Date:  2004-01       Impact factor: 5.899

  10 in total
  1 in total

1.  The new Neyman type A generalized odd log-logistic-G-family with cure fraction.

Authors:  Valdemiro P Vigas; Edwin M M Ortega; Gauss M Cordeiro; Adriano K Suzuki; Giovana O Silva
Journal:  J Appl Stat       Date:  2021-05-03       Impact factor: 1.416

  1 in total

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