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Multivariate Survival Models with a Mixture of Positive Stable Frailties

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  • Nalini Ravishanker

    (University of Connecticut)

  • Dipak K. Dey

    (University of Connecticut)

Abstract

In this paper, we describe models for dependent multivariate survival data using finite mixtures of positive stable frailty distributions. We investigate the cross-ratio function as a local measure of association. We estimate the parameters in the stable mixture together with the parameters of the (conditional) proportional hazards model in a Bayesian framework using Markov chain Monte Carlo algorithms. We illustrate the methodology using data on kidney infections.

Suggested Citation

  • Nalini Ravishanker & Dipak K. Dey, 2000. "Multivariate Survival Models with a Mixture of Positive Stable Frailties," Methodology and Computing in Applied Probability, Springer, vol. 2(3), pages 293-308, September.
    • Handle: RePEc:spr:metcap:v:2:y:2000:i:3:d:10.1023_a:1010033329399
    DOI: 10.1023/A:1010033329399
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    References listed on IDEAS

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    1. Stephen G. Walker & Bani K. Mallick, 1997. "Hierarchical Generalized Linear Models and Frailty Models with Bayesian Nonparametric Mixing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 845-860.
    2. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
    3. Zuqiang Qiou & Nalini Ravishanker & Dipak K. Dey, 1999. "Multivariate Survival Analysis with Positive Stable Frailties," Biometrics, The International Biometric Society, vol. 55(2), pages 637-644, June.
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