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A simulation study of different correlated frailty models and estimation strategies

Author

Listed:
  • Andreas Wienke

    (Max Planck Institute for Demographic Research, Rostock, Germany)

  • Konstantin G. Arbeev

    (Max Planck Institute for Demographic Research, Rostock, Germany)

  • Isabella Locatelli

    (Max Planck Institute for Demographic Research, Rostock, Germany)

  • Anatoli I. Yashin

    (Max Planck Institute for Demographic Research, Rostock, Germany)

Abstract

Frailty models are becoming more and more popular in the area of multivariate survival analysis. In particular, shared frailty models are often used despite their limitations. To overcome the disadvantages of shared frailty models numerous correlated frailty models were established during the last decade. In the present study we examine correlated frailty models, especially the behavior of the parameter estimates when using different estimation strategies. Three different frailty models are considered: the gamma model and two versions of the log-normal model. The traditional maximum likelihood procedure of parameter estimation in the gamma case with an explicit available likelihood function is compared with maximum likelihood methods based on numerical integration and a Bayesian approach using MCMC methods with the help of a comprehensive simulation study. A strong dependence between the two parameter estimates (variance and correlation of frailties) in the multivariate correlated frailty model is detected and analyzed in detail.

Suggested Citation

  • Andreas Wienke & Konstantin G. Arbeev & Isabella Locatelli & Anatoli I. Yashin, 2003. "A simulation study of different correlated frailty models and estimation strategies," MPIDR Working Papers WP-2003-018, Max Planck Institute for Demographic Research, Rostock, Germany.
    • Handle: RePEc:dem:wpaper:wp-2003-018
    DOI: 10.4054/MPIDR-WP-2003-018
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    References listed on IDEAS

    as
    1. Samuli Ripatti & Juni Palmgren, 2000. "Estimation of Multivariate Frailty Models Using Penalized Partial Likelihood," Biometrics, The International Biometric Society, vol. 56(4), pages 1016-1022, December.
    2. Bolstad W. M & Manda S. O, 2001. "Investigating Child Mortality in Malawi Using Family and Community Random Effects: A Bayesian Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 12-19, March.
    3. Lillard, Lee A., 1993. "Simultaneous equations for hazards : Marriage duration and fertility timing," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 189-217, March.
    4. Lee Lillard & Michael Brien & Linda Waite, 1995. "Premarital cohabitation and subsequent marital," Demography, Springer;Population Association of America (PAA), vol. 32(3), pages 437-457, August.
    5. Panis, C.W.A. & Lillard, L.A., 1996. "Child Mortality in Malaysia. Ethnic Differences and the Recent Decline," Papers 96-04, RAND - Reprint Series.
    6. Andreas Wienke, 2003. "Frailty models," MPIDR Working Papers WP-2003-032, Max Planck Institute for Demographic Research, Rostock, Germany.
    7. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
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    Cited by:

    1. Kheiri, Soleiman & Kimber, Alan & Reza Meshkani, Mohammad, 2007. "Bayesian analysis of an inverse Gaussian correlated frailty model," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5317-5326, July.
    2. Andreas Wienke, 2003. "Frailty models," MPIDR Working Papers WP-2003-032, Max Planck Institute for Demographic Research, Rostock, Germany.

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    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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