Literature DB >> 10949859

A comparison of frailty and other models for bivariate survival data.

S K Sahu1, D K Dey.   

Abstract

Multivariate survival data arise when each study subject may experience multiple events or when study subjects are clustered into groups. Statistical analyses of such data need to account for the intra-cluster dependence through appropriate modeling. Frailty models are the most popular for such failure time data. However, there are other approaches which model the dependence structure directly. In this article, we compare the frailty models for bivariate data with the models based on bivariate exponential and Weibull distributions. Bayesian methods provide a convenient paradigm for comparing the two sets of models we consider. Our techniques are illustrated using two examples. One simulated example demonstrates model choice methods developed in this paper and the other example, based on a practical data set of onset of blindness among patients with diabetic Retinopathy, considers Bayesian inference using different models.

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Year:  2000        PMID: 10949859     DOI: 10.1023/a:1009633524403

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


  6 in total

1.  Modelling paired survival data with covariates.

Authors:  W J Huster; R Brookmeyer; S G Self
Journal:  Biometrics       Date:  1989-03       Impact factor: 2.571

2.  A Monte Carlo method for Bayesian inference in frailty models.

Authors:  D G Clayton
Journal:  Biometrics       Date:  1991-06       Impact factor: 2.571

3.  Flexible Bayesian modelling for survival data.

Authors:  P Gustafson
Journal:  Lifetime Data Anal       Date:  1998       Impact factor: 1.588

4.  A Weibull regression model with gamma frailties for multivariate survival data.

Authors:  S K Sahu; D K Dey; H Aslanidou; D Sinha
Journal:  Lifetime Data Anal       Date:  1997       Impact factor: 1.588

5.  Large hierarchical Bayesian analysis of multivariate survival data.

Authors:  P Gustafson
Journal:  Biometrics       Date:  1997-03       Impact factor: 2.571

6.  Semiparametric Marshall-Olkin models applied to the occurrence of metastases at multiple sites after breast cancer.

Authors:  J P Klein; N Keiding; C Kamby
Journal:  Biometrics       Date:  1989-12       Impact factor: 2.571

  6 in total
  7 in total

1.  Semiparametric proportional odds models for spatially correlated survival data.

Authors:  Sudipto Banerjee; Dipak K Dey
Journal:  Lifetime Data Anal       Date:  2005-06       Impact factor: 1.588

2.  Bivariate survival modeling: a Bayesian approach based on Copulas.

Authors:  José S Romeo; Nelson I Tanaka; Antonio C Pedroso-de-Lima
Journal:  Lifetime Data Anal       Date:  2006-07-26       Impact factor: 1.588

3.  Accelerated test system strength models based on Birnbaum-Saunders distribution: a complete Bayesian analysis and comparison.

Authors:  S K Upadhyay; Bhaswati Mukherjee; Ashutosh Gupta
Journal:  Lifetime Data Anal       Date:  2009-03-03       Impact factor: 1.588

4.  Statistical analysis of bivariate failure time data with Marshall-Olkin Weibull models.

Authors:  Yang Li; Jianguo Sun; Shuguang Song
Journal:  Comput Stat Data Anal       Date:  2012-06       Impact factor: 1.681

5.  Bayesian Computational Methods for Sampling from the Posterior Distribution of a Bivariate Survival Model, Based on AMH Copula in the Presence of Right-Censored Data.

Authors:  Erlandson Ferreira Saraiva; Adriano Kamimura Suzuki; Luis Aparecido Milan
Journal:  Entropy (Basel)       Date:  2018-08-27       Impact factor: 2.524

6.  Breast cancer mortality in Saudi Arabia: Modelling observed and unobserved factors.

Authors:  Refah Mohammed Alotaibi; Hoda Ragab Rezk; Consul Iworikumo Juliana; Chris Guure
Journal:  PLoS One       Date:  2018-10-22       Impact factor: 3.240

7.  Bayesian frailty modeling of correlated survival data with application to under-five mortality.

Authors:  Refah M Alotaibi; Hoda Ragab Rezk; Chris Guure
Journal:  BMC Public Health       Date:  2020-09-21       Impact factor: 3.295

  7 in total

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