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Variance components models for survival data

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  • J. H. Petersen
  • P. K. Andersen
  • R.D. Gill

Abstract

Extensions of the Cox proportional hazards model for survival data are studied where allowance is made for unobserved heterogeneity and for correlation between the life times of several individuals. The extended models are frailty models inspired by Yashin et al. (1995). Estimation is carried out using the EM algorithm. Inference is discussed and potential applications are outlined, in particular to statistical research in human genetics using twin data or adoption data, aimed at separating the effects of genetic and environmental factors on mortality.

Suggested Citation

  • J. H. Petersen & P. K. Andersen & R.D. Gill, 1996. "Variance components models for survival data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 50(1), pages 193-211, March.
    • Handle: RePEc:bla:stanee:v:50:y:1996:i:1:p:193-211
    DOI: 10.1111/j.1467-9574.1996.tb01487.x
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    Cited by:

    1. 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.
    2. 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.
    3. Jaap H. Abbring & Gerard J. Van Den Berg, 2007. "The unobserved heterogeneity distribution in duration analysis," Biometrika, Biometrika Trust, vol. 94(1), pages 87-99.
    4. Keith Humphreys, 1998. "The Latent Markov Chain with Multivariate Random Effects," Sociological Methods & Research, , vol. 26(3), pages 269-299, February.

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