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Survival Models in Health Economic Evaluations: Balancing Fit and Parsimony to Improve Prediction


  • Jackson Christopher H

    (MRC Biostatistics Unit)

  • Sharples Linda D

    (MRC Biostatistics Unit)

  • Thompson Simon G

    (MRC Biostatistics Unit)


Health economic decision models compare costs and health effects of different interventions over the long term and usually incorporate survival data. Since survival is often extrapolated beyond the range of the data, inaccurate model specification can result in very different policy decisions. However, in this area, flexible survival models are rarely considered, and model uncertainty is rarely accounted for. In this article, various survival distributions are applied in a decision model for oral cancer screening. Flexible parametric models are compared with Bayesian semiparametric models, in which the baseline hazard can be made arbitrarily complex while still enabling survival to be extrapolated. A fully Bayesian framework is used for all models so that uncertainties can be easily incorporated in estimates of long-term costs and effects. The fit and predictive ability of both parametric and semiparametric models are compared using the deviance information criterion in order to account for model uncertainty in the cost-effectiveness analysis. Under the Bayesian semiparametric models, some smoothing of the hazard function is required to obtain adequate predictive ability and avoid sensitivity to the choice of prior. We determine that one flexible parametric survival model fits substantially better than the others considered in the oral cancer example.

Suggested Citation

  • Jackson Christopher H & Sharples Linda D & Thompson Simon G, 2010. "Survival Models in Health Economic Evaluations: Balancing Fit and Parsimony to Improve Prediction," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-31, October.
  • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:1:n:34

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    References listed on IDEAS

    1. Maria De Iorio & Wesley O. Johnson & Peter Müller & Gary L. Rosner, 2009. "Bayesian Nonparametric Nonproportional Hazards Survival Modeling," Biometrics, The International Biometric Society, vol. 65(3), pages 762-771, September.
    2. Christopher H. Jackson & Linda D. Sharples & Simon G. Thompson, 2010. "Structural and parameter uncertainty in Bayesian cost-effectiveness models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 233-253.
    3. Stephen Walker & Bani K. Mallick, 1999. "A Bayesian Semiparametric Accelerated Failure Time Model," Biometrics, The International Biometric Society, vol. 55(2), pages 477-483, June.
    4. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
    5. Christopher H. Jackson & Simon G. Thompson & Linda D. Sharples, 2009. "Accounting for uncertainty in health economic decision models by using model averaging," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 383-404.
    6. Francisco Louzada-Neto, 1999. "Polyhazard Models for Lifetime Data," Biometrics, The International Biometric Society, vol. 55(4), pages 1281-1285, December.
    7. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika van der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639.
    8. A. E. Ades & Karl Claxton & Mark Sculpher, 2006. "Evidence synthesis, parameter correlation and probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 373-381.
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