IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v38y2018i7p834-848.html
   My bibliography  Save this article

Bayesian Multiparameter Evidence Synthesis to Inform Decision Making: A Case Study in Metastatic Hormone-Refractory Prostate Cancer

Author

Listed:
  • Sze Huey Tan
  • Keith R. Abrams
  • Sylwia Bujkiewicz

Abstract

In health technology assessment, decisions are based on complex cost-effectiveness models that require numerous input parameters. When not all relevant estimates are available, the model may have to be simplified. Multiparameter evidence synthesis combines data from diverse sources of evidence, which results in obtaining estimates required in clinical decision making that otherwise may not be available. We demonstrate how bivariate meta-analysis can be used to predict an unreported estimate of a treatment effect enabling implementation of a multistate Markov model, which otherwise needs to be simplified. To illustrate this, we used an example of cost-effectiveness analysis for docetaxel in combination with prednisolone in metastatic hormone-refractory prostate cancer. Bivariate meta-analysis was used to model jointly available data on treatment effects on overall survival and progression-free survival (PFS) to predict the unreported effect on PFS in a study evaluating docetaxel with prednisolone. The predicted treatment effect on PFS enabled implementation of a 3-state Markov model comprising stable disease, progressive disease, and dead states, while lack of the estimate restricted the model to a 2-state model (with alive and dead states). The 2-state and 3-state models were compared by calculating the incremental cost-effectiveness ratio (which was much lower in the 3-state model: £22,148 per quality-adjusted life year gained compared to £30,026 obtained from the 2-state model) and the expected value of perfect information (which increased with the 3-state model). The 3-state model has the advantage of distinguishing surviving patients who progressed from those who did not progress. Hence, the use of advanced meta-analytic techniques allowed obtaining relevant parameter estimates to populate a model describing disease pathway in more detail while helping to prevent valuable clinical data from being discarded.

Suggested Citation

  • Sze Huey Tan & Keith R. Abrams & Sylwia Bujkiewicz, 2018. "Bayesian Multiparameter Evidence Synthesis to Inform Decision Making: A Case Study in Metastatic Hormone-Refractory Prostate Cancer," Medical Decision Making, , vol. 38(7), pages 834-848, October.
    • Handle: RePEc:sae:medema:v:38:y:2018:i:7:p:834-848
    DOI: 10.1177/0272989X18788537
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X18788537
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X18788537?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. Catrin Tudur Smith & Kerry Dwan & Douglas G Altman & Mike Clarke & Richard Riley & Paula R Williamson, 2014. "Sharing Individual Participant Data from Clinical Trials: An Opinion Survey Regarding the Establishment of a Central Repository," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-8, May.
    2. Karl Claxton & John Posnett, 1996. "An economic approach to clinical trial design and research priority‐setting," Health Economics, John Wiley & Sons, Ltd., vol. 5(6), pages 513-524, November.
    3. Karl Claxton & John Posnett, "undated". "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
    Full references (including those not matched with items on IDEAS)

    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. Neil Hawkins & Mark Sculpher & David Epstein, 2005. "Cost-Effectiveness Analysis of Treatments for Chronic Disease: Using R to Incorporate Time Dependency of Treatment Response," Medical Decision Making, , vol. 25(5), pages 511-519, September.
    2. Mark Strong & Jeremy E. Oakley, 2013. "An Efficient Method for Computing Single-Parameter Partial Expected Value of Perfect Information," Medical Decision Making, , vol. 33(6), pages 755-766, August.
    3. Rachael L. Fleurence, 2007. "Setting priorities for research: a practical application of 'payback' and expected value of information," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1345-1357.
    4. Sofia Dias & Alex J. Sutton & Nicky J. Welton & A. E. Ades, 2013. "Evidence Synthesis for Decision Making 6," Medical Decision Making, , vol. 33(5), pages 671-678, July.
    5. Samer A. Kharroubi & Alan Brennan & Mark Strong, 2011. "Estimating Expected Value of Sample Information for Incomplete Data Models Using Bayesian Approximation," Medical Decision Making, , vol. 31(6), pages 839-852, November.
    6. Nicky J. Welton & Jason J. Madan & Deborah M. Caldwell & Tim J. Peters & Anthony E. Ades, 2014. "Expected Value of Sample Information for Multi-Arm Cluster Randomized Trials with Binary Outcomes," Medical Decision Making, , vol. 34(3), pages 352-365, April.
    7. Mark Strong & Jeremy E. Oakley & Alan Brennan & Penny Breeze, 2015. "Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample," Medical Decision Making, , vol. 35(5), pages 570-583, July.
    8. Josh J. Carlson & Rahber Thariani & Josh Roth & Julie Gralow & N. Lynn Henry & Laura Esmail & Pat Deverka & Scott D. Ramsey & Laurence Baker & David L. Veenstra, 2013. "Value-of-Information Analysis within a Stakeholder-Driven Research Prioritization Process in a US Setting: An Application in Cancer Genomics," Medical Decision Making, , vol. 33(4), pages 463-471, May.
    9. Mark Strong & Jeremy E. Oakley & Alan Brennan, 2014. "Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample," Medical Decision Making, , vol. 34(3), pages 311-326, April.
    10. Jeff Miller & Rolf Ulrich, 2019. "The quest for an optimal alpha," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-13, January.
    11. Hawre Jalal & Jeremy D. Goldhaber-Fiebert & Karen M. Kuntz, 2015. "Computing Expected Value of Partial Sample Information from Probabilistic Sensitivity Analysis Using Linear Regression Metamodeling," Medical Decision Making, , vol. 35(5), pages 584-595, July.
    12. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225, November.
    13. Jennifer Uyei & R. Scott Braithwaite, 2016. "Are There Scenarios When the Use of Non–Placebo-Control Groups in Experimental Trial Designs Increase Expected Value to Society?," Medical Decision Making, , vol. 36(1), pages 20-30, January.
    14. Manuel A. Espinoza & Andrea Manca & Karl Claxton & Mark J. Sculpher, 2014. "The Value of Heterogeneity for Cost-Effectiveness Subgroup Analysis," Medical Decision Making, , vol. 34(8), pages 951-964, November.
    15. Anirban Basu & David Meltzer, 2018. "Decision Criterion and Value of Information Analysis: Optimal Aspirin Dosage for Secondary Prevention of Cardiovascular Events," Medical Decision Making, , vol. 38(4), pages 427-438, May.
    16. Franck Maunoury & Anastasiia Motrunich & Maria Palka-Santini & Stéphanie F Bernatchez & Stéphane Ruckly & Jean-François Timsit, 2015. "Cost-Effectiveness Analysis of a Transparent Antimicrobial Dressing for Managing Central Venous and Arterial Catheters in Intensive Care Units," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-14, June.
    17. Ana P. Johnson-Masotti & Purushottam W. Laud & Raymond G. Hoffmann & Matthew J. Hayat & Steven D. Pinkerton, 2004. "A Bayesian Approach to Net Health Benefits: An Illustration and Application to Modeling HIV Prevention," Medical Decision Making, , vol. 24(6), pages 634-653, November.
    18. Martin E. Backhouse, 1998. "An investment appraisal approach to clinical trial design," Health Economics, John Wiley & Sons, Ltd., vol. 7(7), pages 605-619, November.
    19. Lazaros Andronis & Lucinda J. Billingham & Stirling Bryan & Nicholas D. James & Pelham M. Barton, 2016. "A Practical Application of Value of Information and Prospective Payback of Research to Prioritize Evaluative Research," Medical Decision Making, , vol. 36(3), pages 321-334, April.
    20. Michael Fairley & Lauren E. Cipriano & Jeremy D. Goldhaber-Fiebert, 2020. "Optimal Allocation of Research Funds under a Budget Constraint," Medical Decision Making, , vol. 40(6), pages 797-814, August.

    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:38:y:2018:i:7:p:834-848. 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.