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Assessing Gamma Frailty Models for Clustered Failure Time Data

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Lifetime Data: Models in Reliability and Survival Analysis
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Abstract

Proportional hazards frailty models use a random effect, so called frailty, to construct association for clustered failure time data. It is customary to assume that the random frailty follows a gamma distribution. In this paper, we propose a graphical method for assessing adequacy of the proportional hazards frailty models. In particular, we focus on the assessment of the gamma distribution assumption for the frailties. We calculate the average of the posterior expected frailties at several followup time points and compare it at these time points to 1, the known mean frailty. Large discrepancies indicate lack of fit. To aid in assessing the goodness of fit, we derive and estimate the standard error of the mean of the posterior expected frailties at each time point examined. We give an example to illustrate the proposed methodology.

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References

  • Aalen, O.O. (1988). Heterogeneity in survival analysis. Statistics in Medicine 7, 1121–1137.

    Article  Google Scholar 

  • Anderson, J.E., Louis, T.A., Holm, N.V. (1992). Time dependent association for bivariate survival distributions. Journal of the American Statistical Association 87, 641–650.

    Article  MathSciNet  Google Scholar 

  • Arjas, E. (1988). A graphical method for assessing goodness of fit in Cox’s proportional hazards model. Journal of the American Statistical Association 83, 204–212.

    Article  Google Scholar 

  • Butler SM, Louis TA (1992). Random effects models with non-parametric priors. Statistics in Medicine, 11, 1981–2000.

    Article  Google Scholar 

  • Clayton, D.G. (1978). A model for association in bivariate life tables and its application in Epidemiological studies of familial tendency in chronic disease incidence. Biometrika 65, 141–151.

    Article  MathSciNet  MATH  Google Scholar 

  • Clayton, D.G. and Cuzick, J. (1985). Multivariate associations of the proportional hazards model. Journal of the Royal Statistical Society. Ser.. 4148, 82–108.

    Article  MathSciNet  Google Scholar 

  • Clayton, D.G. (1991). A Monte-Carlo methods for Bayesian inference in frailty models. Biometrics 47, 467–485.

    Article  Google Scholar 

  • Cox, D.R. and Hinkley, D.V. (1974). Theoretical Statistics. London: Chapman and Hall. GAUSS system Version 3.0 ( 1992 ), Kent, Washington: Aptech Systems, Inc.

    Google Scholar 

  • Gordan, T. and Kannel, W.E. (1968). Introduction and general background in the Framingham study-the Framingham study, sections 1 and 2. National Heart, Lung and Blood Institute, Bethesda, MD.

    Google Scholar 

  • Heckman, J.J. and Singer, B. (1984). A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica 52, 271–320.

    Article  MathSciNet  MATH  Google Scholar 

  • Hougaard, P. (1986a). Survival models for heterogeneous populations derived from stable distributions. Biometrika 73, 387–396.

    Article  MathSciNet  MATH  Google Scholar 

  • Hougaard, P. (1986b). A class of multivariate failure time distributions. Biometrika 73, 671–678.

    MathSciNet  MATH  Google Scholar 

  • Laird, N.M. (1978). Nonparametric maximum likelihood estimation of a mixing distribution. Journal of the American Statistical Association 73, 805–811.

    Article  MathSciNet  MATH  Google Scholar 

  • Laird, N.M. and Louis, T.A. (1991). Smoothing the non-parametric estimate of a prior distribution by roughening: A computational study. Computational Stat. and Data Analysis 12, 27–37.

    Article  MathSciNet  MATH  Google Scholar 

  • Manton, K.G., Stallard, E., and Vaupel J.W. (1986). Alternative models for the heterogeneity of mortality risks among the aged. Journal of the American Statistical Association 81, 635–644.

    Article  Google Scholar 

  • Oakes, D. (1989). Bivariate survival models induced by frailties. Journal of the American Statistical Association 84, 487–493.

    Article  MathSciNet  MATH  Google Scholar 

  • Shih, J.H. and Louis, T.A. (1993). Inferences on the association parameter in copula models for bivariate survival data. Technical report, University of Minnesota.

    Google Scholar 

  • Shih, J.H. and Louis, T.A. (1994). Assessing gamma frailty models for clustered failure time data. Technical report, University of Minnesota.

    Google Scholar 

  • Therneau, T.M., Grambsch, P.M., and Fleming, T.R. (1990). Martingale-based residuals for survival models. Biometrika 77, 147–160.

    Article  MathSciNet  MATH  Google Scholar 

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© 1996 Springer Science+Business Media Dordrecht

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Shih, J.H., Louis, T.A. (1996). Assessing Gamma Frailty Models for Clustered Failure Time Data. In: Jewell, N.P., Kimber, A.C., Lee, ML.T., Whitmore, G.A. (eds) Lifetime Data: Models in Reliability and Survival Analysis. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5654-8_39

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  • DOI: https://doi.org/10.1007/978-1-4757-5654-8_39

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4753-6

  • Online ISBN: 978-1-4757-5654-8

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