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Dependent Hazards in Multivariate Survival Problems

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  • Yashin, Anatoli I.
  • Iachine, Ivan A.

Abstract

A new class of bivariate survival distributions is constructed from a given family of survival distributions. The properties of these distributions are analyzed. It is shown that the same bivariate survival function can be derived using two radically different concepts: one involves transformation of the well-known bivariate survival function; the other involves correlated stochastic hazards. The new conditions that guarantee negative associations of life spans are derived. An exponential representation of the survival function for two related individuals is derived in terms of the conditional distribution of the stochastic hazards among survivors. Versions of the multivariate correlated gamma-frailty model are investigated.

Suggested Citation

  • Yashin, Anatoli I. & Iachine, Ivan A., 1999. "Dependent Hazards in Multivariate Survival Problems," Journal of Multivariate Analysis, Elsevier, vol. 71(2), pages 241-261, November.
    • Handle: RePEc:eee:jmvana:v:71:y:1999:i:2:p:241-261
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    References listed on IDEAS

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    Cited by:

    1. Jie Huang & Haiming Zhou & Nader Ebrahimi, 2022. "Bayesian Bivariate Cure Rate Models Using Copula Functions," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(3), pages 1-9, May.
    2. Finkelstein, M. S., 2003. "On one class of bivariate distributions," Statistics & Probability Letters, Elsevier, vol. 65(1), pages 1-6, October.
    3. Rounak Dey & Wei Zhou & Tuomo Kiiskinen & Aki Havulinna & Amanda Elliott & Juha Karjalainen & Mitja Kurki & Ashley Qin & Seunggeun Lee & Aarno Palotie & Benjamin Neale & Mark Daly & Xihong Lin, 2022. "Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. P. Sankaran & V. Gleeja, 2008. "Proportional reversed hazard and frailty models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(3), pages 333-342, November.

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