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A Bayesian approach to sensitivity analysis

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  • James C. Felli
  • Gordon B. Hazen

Abstract

Sensitivity analysis has traditionally been applied to decision models to quantify the stability of a preferred alternative to parametric variation. In the health literature, sensitivity measures have traditionally been based upon distance metrics, payoff variations, and probability measures. We advocate a new approach based on information value and argue that such an approach is better suited to address the decision‐maker's real concerns. We provide an example comparing conventional sensitivity analysis to one based on information value.This article is a US government work and is in the public domain in the United States

Suggested Citation

  • James C. Felli & Gordon B. Hazen, 1999. "A Bayesian approach to sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 263-268, May.
    • Handle: RePEc:wly:hlthec:v:8:y:1999:i:3:p:263-268
    DOI: 10.1002/(SICI)1099-1050(199905)8:3<263::AID-HEC426>3.0.CO;2-S
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    Cited by:

    1. 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, April.
    2. Karnon, Jonathan, 2002. "Planning the efficient allocation of research funds: an adapted application of a non-parametric Bayesian value of information analysis," Health Policy, Elsevier, vol. 61(3), pages 329-347, September.
    3. Doug Coyle & Jeremy Oakley, 2008. "Estimating the expected value of partial perfect information: a review of methods," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 9(3), pages 251-259, August.
    4. Douglas Coyle & Martin J. Buxton & Bernie J. O'Brien, 2003. "Stratified cost‐effectiveness analysis: a framework for establishing efficient limited use criteria," Health Economics, John Wiley & Sons, Ltd., vol. 12(5), pages 421-427, May.
    5. Borgonovo, Emanuele & Hazen, Gordon B. & Jose, Victor Richmond R. & Plischke, Elmar, 2021. "Probabilistic sensitivity measures as information value," European Journal of Operational Research, Elsevier, vol. 289(2), pages 595-610.
    6. Anna Heath & Ioanna Manolopoulou & Gianluca Baio, 2017. "A Review of Methods for Analysis of the Expected Value of Information," Medical Decision Making, , vol. 37(7), pages 747-758, October.
    7. Whitney, Cory W. & Tabuti, John R.S. & Hensel, Oliver & Yeh, Ching-Hua & Gebauer, Jens & Luedeling, Eike, 2017. "Homegardens and the future of food and nutrition security in southwest Uganda," Agricultural Systems, Elsevier, vol. 154(C), pages 133-144.
    8. 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.
    9. Nicky Welton & A. E. Ades, 2012. "Research Decisions In The Face Of Heterogeneity: What Can A New Study Tell Us?," Health Economics, John Wiley & Sons, Ltd., vol. 21(10), pages 1196-1200, October.
    10. Elizabeth Fenwick & Karl Claxton & Mark Sculpher & Andrew Briggs, 2000. "Improving the efficiency and relevance of health technology assessent: the role of iterative decision analytic modelling," Working Papers 179chedp, Centre for Health Economics, University of York.
    11. Emanuele Borgonovo & Alessandra Cillo & Curtis L. Smith, 2018. "On the Relationship between Safety and Decision Significance," Risk Analysis, John Wiley & Sons, vol. 38(8), pages 1541-1558, August.
    12. Wesley J. Marrero & Mariel S. Lavieri & Jeremy B. Sussman, 2021. "Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases," Health Care Management Science, Springer, vol. 24(1), pages 1-25, March.
    13. Emanuele Borgonovo & Alessandra Cillo, 2017. "Deciding with Thresholds: Importance Measures and Value of Information," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 1828-1848, October.
    14. Anna Heath & Ioanna Manolopoulou & Gianluca Baio, 2018. "Efficient Monte Carlo Estimation of the Expected Value of Sample Information Using Moment Matching," Medical Decision Making, , vol. 38(2), pages 163-173, February.
    15. Jonathan Karnon, 2003. "Alternative decision modelling techniques for the evaluation of health care technologies: Markov processes versus discrete event simulation," Health Economics, John Wiley & Sons, Ltd., vol. 12(10), pages 837-848, October.
    16. A. E. Ades & A. J. Sutton, 2006. "Multiparameter evidence synthesis in epidemiology and medical decision‐making: current approaches," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 5-35, January.
    17. Douglas Coyle, 2003. "Determining the optimal combinations of mutually exclusive interventions: a response to Hutubessy and colleagues," Health Economics, John Wiley & Sons, Ltd., vol. 12(2), pages 159-162, February.
    18. Carlo Drago & Matteo Ruggeri, 2019. "Setting research priorities in the field of emergency management: which piece of information are you willing to pay more?," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 2103-2115, July.
    19. Fumie Yokota & Kimberly M. Thompson, 2004. "Value of Information Literature Analysis: A Review of Applications in Health Risk Management," Medical Decision Making, , vol. 24(3), pages 287-298, June.
    20. H Pilgrim & P Tappenden & J Chilcott & M Bending & P Trueman & A Shorthouse & J Tappenden, 2009. "The costs and benefits of bowel cancer service developments using discrete event simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(10), pages 1305-1314, October.
    21. Eric Jutkowitz & Fernando Alarid-Escudero & Hyon K. Choi & Karen M. Kuntz & Hawre Jalal, 2017. "Prioritizing Future Research on Allopurinol and Febuxostat for the Management of Gout: Value of Information Analysis," PharmacoEconomics, Springer, vol. 35(10), pages 1073-1085, October.
    22. F. J. Vázquez‐Polo & M. A. Negrín Hernández & B. González López‐Valcárcel, 2005. "Using covariates to reduce uncertainty in the economic evaluation of clinical trial data," Health Economics, John Wiley & Sons, Ltd., vol. 14(6), pages 545-557, June.

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