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Journal ArticleDOI

A Comparison of Frailty and Other Models for Bivariate Survival Data

TLDR
This article compares theFrailty models for bivariate data with the models based on bivariateexponential and Weibull distributions and considers Bayesian inference using different models.
Abstract
Multivariate survival data arise when eachstudy subject may experience multiple events or when study subjectsare clustered into groups Statistical analyses of such dataneed to account for the intra-cluster dependence through appropriatemodeling Frailty models are the most popular for such failuretime data However, there are other approaches which model thedependence structure directly In this article, we compare thefrailty models for bivariate data with the models based on bivariateexponential and Weibull distributions Bayesian methods providea convenient paradigm for comparing the two sets of models weconsider Our techniques are illustrated using two examplesOne simulated example demonstrates model choice methods developedin this paper and the other example, based on a practical dataset of onset of blindness among patients with diabetic Retinopathy,considers Bayesian inference using different models

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Journal ArticleDOI

Skewed multivariate models related to hidden truncation and/or selective reporting

TL;DR: The univariate skew-normal distribution was introduced by Azzalini in 1985 as a natural extension of the classical normal density to accommodate asymmetry and was extended to include the multivariate analog of the skew normal by Arnold et al. as mentioned in this paper.
Journal ArticleDOI

Semiparametric proportional odds models for spatially correlated survival data.

TL;DR: A Bayesian hierarchical model for capturing spatial heterogeneity within the framework of proportional odds is developed, deemed more appropriate when a substantial percentage of subjects enjoy prolonged survival.
Journal ArticleDOI

Bayesian analysis of an inverse Gaussian correlated frailty model

TL;DR: A Bayesian analysis of a correlated frailty model is discussed in the context of inverse Gaussian frailty and an MCMC approach is adopted and the deviance information criterion is used to compare models.
Journal ArticleDOI

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

TL;DR: Though the prevalence of breast cancer mortality among men is lower than that of women, men had a higher risk of death and an intensive health education programme for both men and women is recommended.
Journal ArticleDOI

Bivariate survival modeling: a Bayesian approach based on Copulas.

TL;DR: This paper reviews some of the recent work that has been appeared for copula model for bivariate survival data and proposes a Bayesian modeling approach, which is very flexible with respect to the choice of marginal distributions.
References
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Journal ArticleDOI

Estimating the Dimension of a Model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.

Estimating the dimension of a model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI

Sampling-Based Approaches to Calculating Marginal Densities

TL;DR: In this paper, three sampling-based approaches, namely stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm, are compared and contrasted in relation to various joint probability structures frequently encountered in applications.
Journal Article

Sampling-based approaches to calculating marginal densities

TL;DR: Stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm can be viewed as three alternative sampling- (or Monte Carlo-) based approaches to the calculation of numerical estimates of marginal probability distributions.
Journal ArticleDOI

Approximate inference in generalized linear mixed models

TL;DR: In this paper, generalized linear mixed models (GLMM) are used to estimate the marginal quasi-likelihood for the mean parameters and the conditional variance for the variances, and the dispersion matrix is specified in terms of a rank deficient inverse covariance matrix.
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