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Direct Incorporation of Expert Opinion into Parametric Survival Models to Inform Survival Extrapolation

Author

Listed:
  • Philip Cooney

    (School of Computer Science and Statistics, O’Reilly Institute, Trinity College Dublin, Dublin 2, Ireland)

  • Arthur White

    (School of Computer Science and Statistics, O’Reilly Institute, Trinity College Dublin, Dublin 2, Ireland)

Abstract

Background In decision modeling with time-to-event data, there are a variety of parametric models that can be used to extrapolate the survival function. Each model implies a different hazard function, and in situations in which there is moderate censoring, this can result in quite different survival projections. External information such as expert opinion on long-term survival can more accurately characterize the uncertainty in these extrapolations. Objective We present a general and easily implementable approach to incorporate various types of expert opinions into parametric survival models, focusing on opinions about survival at various landmark time points. Methods Expert opinion is incorporated into parametric survival models using Bayesian and frequentist approaches. In the Bayesian method, expert opinion is included through a loss function and in the frequentist approach by penalizing the likelihood function, although in both cases the core approach is the same. The issue of aggregating multiple expert opinions is also considered. Results We apply this method to data from a leukemia trial and use previously elicited expert opinion on survival probabilities for that particular trial population at years 4 and 5 to inform our analysis. We take a robust approach to modeling expert opinion by using pooled distributions and fit a broad class of parametric models to the data. We also assess statistical goodness of fit of the models to both the observed data and expert opinion. Conclusions Expert opinions can be implemented in a straightforward manner using this novel approach; however, more work is required on the correct elicitation of these quantities. Highlights Presentation of a novel and open-source method to incorporate expert opinion into decision modeling. Extends upon earlier work in that expert opinion can be incorporated into a wide range of parametric models. Provides methodological guidance for directly including expert opinion in decision modeling, which is a research focus area in NICE TSD 21. 1

Suggested Citation

  • Philip Cooney & Arthur White, 2023. "Direct Incorporation of Expert Opinion into Parametric Survival Models to Inform Survival Extrapolation," Medical Decision Making, , vol. 43(3), pages 325-336, April.
  • Handle: RePEc:sae:medema:v:43:y:2023:i:3:p:325-336
    DOI: 10.1177/0272989X221150212
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    References listed on IDEAS

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    1. Robert T. Clemen & Robert L. Winkler, 1999. "Combining Probability Distributions From Experts in Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 19(2), pages 187-203, April.
    2. Patricia Guyot & Anthony E. Ades & Matthew Beasley & Béranger Lueza & Jean-Pierre Pignon & Nicky J. Welton, 2017. "Extrapolation of Survival Curves from Cancer Trials Using External Information," Medical Decision Making, , vol. 37(4), pages 353-366, May.
    3. 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, October.
    4. 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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