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Extrapolating Parametric Survival Models in Health Technology Assessment Using Model Averaging: A Simulation Study

Author

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  • Daniel Gallacher

    (University of Warwick, Warwick Medical School, Coventry, UK)

  • Peter Kimani

    (University of Warwick, Warwick Medical School, Coventry, UK)

  • Nigel Stallard

    (University of Warwick, Warwick Medical School, Coventry, UK)

Abstract

Previous work examined the suitability of relying on routine methods of model selection when extrapolating survival data in a health technology appraisal setting. Here we explore solutions to improve reliability of restricted mean survival time (RMST) estimates from trial data by assessing model plausibility and implementing model averaging. We compare our previous methods of selecting a model for extrapolation using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Our methods of model averaging include using equal weighting across models falling within established threshold ranges for AIC and BIC and using BIC-based weighted averages. We apply our plausibility assessment and implement model averaging to the output of our previous simulations, where 10,000 runs of 12 trial-based scenarios were examined. We demonstrate that removing implausible models from consideration reduces the mean squared error associated with the restricted mean survival time (RMST) estimate from each selection method and increases the percentage of RMST estimates that were within 10% of the RMST from the parameters of the sampling distribution. The methods of averaging were superior to selecting a single optimal extrapolation, aside from some of the exponential scenarios where BIC already selected the exponential model. The averaging methods with wide criterion-based thresholds outperformed BIC-weighted averaging in the majority of scenarios. We conclude that model averaging approaches should feature more widely in the appraisal of health technologies where extrapolation is influential and considerable uncertainty is present. Where data demonstrate complicated underlying hazard rates, funders should account for the additional uncertainty associated with these extrapolations in their decision making. Extended follow-up from trials should be encouraged and used to review prices of therapies to ensure a fair price is paid.

Suggested Citation

  • Daniel Gallacher & Peter Kimani & Nigel Stallard, 2021. "Extrapolating Parametric Survival Models in Health Technology Assessment Using Model Averaging: A Simulation Study," Medical Decision Making, , vol. 41(4), pages 476-484, May.
    • Handle: RePEc:sae:medema:v:41:y:2021:i:4:p:476-484
    DOI: 10.1177/0272989X21992297
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    References listed on IDEAS

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    1. Jackson, Christopher, 2016. "flexsurv: A Platform for Parametric Survival Modeling in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i08).
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    1. Mathyn Vervaart & Mark Strong & Karl P. Claxton & Nicky J. Welton & Torbjørn Wisløff & Eline Aas, 2022. "An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial," Medical Decision Making, , vol. 42(5), pages 612-625, July.
    2. Taihang Shao & Mingye Zhao & Leyi Liang & Lizheng Shi & Wenxi Tang, 2023. "Impact of Extrapolation Model Choices on the Structural Uncertainty in Economic Evaluations for Cancer Immunotherapy: A Case Study of Checkmate 067," PharmacoEconomics - Open, Springer, vol. 7(3), pages 383-392, May.

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