IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v43y2023i3p325-336.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X221150212
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X221150212?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ash Bullement & Mark Edmondson-Jones & Patricia Guyot & Nicky J. Welton & Gianluca Baio & Matthew Stevenson & Nicholas R. Latimer, 2024. "MPES-R: Multi-Parameter Evidence Synthesis in R for Survival Extrapolation—A Tutorial," PharmacoEconomics, Springer, vol. 42(12), pages 1317-1327, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexina J. Mason & Manuel Gomes & James Carpenter & Richard Grieve, 2021. "Flexible Bayesian longitudinal models for cost‐effectiveness analyses with informative missing data," Health Economics, John Wiley & Sons, Ltd., vol. 30(12), pages 3138-3158, December.
    2. Jason R. W. Merrick, 2009. "Bayesian Simulation and Decision Analysis: An Expository Survey," Decision Analysis, INFORMS, vol. 6(4), pages 222-238, December.
    3. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    4. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    5. Christina Leuker & Thorsten Pachur & Ralph Hertwig & Timothy J. Pleskac, 2019. "Do people exploit risk–reward structures to simplify information processing in risky choice?," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 76-94, August.
    6. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    7. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.
    8. Svetlana V. Tishkovskaya & Paul G. Blackwell, 2021. "Bayesian estimation of heterogeneous environments from animal movement data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    9. Leonardo Oliveira Martins & Hirohisa Kishino, 2010. "Distribution of distances between topologies and its effect on detection of phylogenetic recombination," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 145-159, February.
    10. Tamal Ghosh & Malay Ghosh & Jerry J. Maples & Xueying Tang, 2022. "Multivariate Global-Local Priors for Small Area Estimation," Stats, MDPI, vol. 5(3), pages 1-16, July.
    11. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    12. Wu, Ji & Guo, Mengmeng & Chen, Minghua & Jeon, Bang Nam, 2019. "Market power and risk-taking of banks: Some semiparametric evidence from emerging economies," Emerging Markets Review, Elsevier, vol. 41(C).
    13. repec:jss:jstsof:21:i08 is not listed on IDEAS
    14. Deng, Yaguo, 2016. "Efficiency evaluation of Spanish hotel chains," DES - Working Papers. Statistics and Econometrics. WS 23897, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. Cathy W. S. Chen & Sangyeol Lee, 2017. "Bayesian causality test for integer-valued time series models with applications to climate and crime data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 797-814, August.
    16. Makoto Chikaraishi & Akimasa Fujiwara & Junyi Zhang & Kay Axhausen, 2011. "Identifying variations and co-variations in discrete choice models," Transportation, Springer, vol. 38(6), pages 993-1016, November.
    17. Avner Engel & Shalom Shachar, 2006. "Measuring and optimizing systems' quality costs and project duration," Systems Engineering, John Wiley & Sons, vol. 9(3), pages 259-280, September.
    18. Galatia Cleanthous & Emilio Porcu & Philip White, 2021. "Regularity and approximation of Gaussian random fields evolving temporally over compact two-point homogeneous spaces," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 836-860, December.
    19. Baños-Pino, José F. & Boto-García, David & Zapico, Emma, 2021. "Persistence and dynamics in the efficiency of toll motorways: The Spanish case," Efficiency Series Papers 2021/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    20. Xing Ju Lee & Christopher C. Drovandi & Anthony N. Pettitt, 2015. "Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets," Biometrics, The International Biometric Society, vol. 71(1), pages 198-207, March.
    21. Chaix, Basile & Jouven, Xavier & Thomas, Frédérique & Leal, Cinira & Billaudeau, Nathalie & Bean, Kathy & Kestens, Yan & Jëgo, Bertrand & Pannier, Bruno & Danchin, Nicolas, 2011. "Why socially deprived populations have a faster resting heart rate: Impact of behaviour, life course anthropometry, and biology – the RECORD Cohort Study," Social Science & Medicine, Elsevier, vol. 73(10), pages 1543-1550.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:43:y:2023:i:3:p:325-336. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.