IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v24y2004i6p634-653.html
   My bibliography  Save this article

A Bayesian Approach to Net Health Benefits: An Illustration and Application to Modeling HIV Prevention

Author

Listed:
  • Ana P. Johnson-Masotti

    (Clinical Epidemiology and Biostatistics Department, McMaster University, Hamilton, Ontario, Canada and Department of Psychiatry and Behavior Medicine, Medical College of Wisconsin, Milwaukee)

  • Purushottam W. Laud

    (Division of Biostatistics, Medical College of Wisconsin, Milwaukee)

  • Raymond G. Hoffmann

    (Division of Biostatistics, Medical College of Wisconsin, Milwaukee)

  • Matthew J. Hayat

    (Division of Biostatistics, Medical College of Wisconsin, Milwaukee and National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland)

  • Steven D. Pinkerton

    (Department of Psychiatry and Behavior Medicine, Medical College of Wisconsin, Milwaukee)

Abstract

Purpose. To conduct a cost-effectiveness analysis of HIV prevention when costs and effects cannot be measured directly. To quantify the total estimation of uncertainty due to sampling variability as well as inexact knowledge of HIV transmission parameters. Methods. The authors focus on estimating the incremental net health benefit (INHB) in a randomized trial of HIV prevention with intervention and control conditions. Using a Bernoulli model of HIV transmission, changes in the participants’ risk behaviors are converted into the number of HIV infections averted. A sampling model is used to account for variation in the behavior measurements. Bayes’s theorem and Monte Carlo methods are used to attain the stated objectives. Results. The authors obtained a positive mean INHB of 0.0008, indicating that advocacy training is just slightly favored over the control condition for men, assuming a $50,000 per quality-adjusted life year (QALY) threshold. To be confident of a positive INHB, the decision maker would need to spend more than $100,000 per QALY.

Suggested Citation

  • 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.
  • Handle: RePEc:sae:medema:v:24:y:2004:i:6:p:634-653
    DOI: 10.1177/0272989X04271040
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X04271040
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X04271040?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. Holtgrave, D.R. & Kelly, J.A., 1996. "Preventing HIV/AIDS among high-risk urban women: The cost-effectiveness of a behavioral group intervention," American Journal of Public Health, American Public Health Association, vol. 86(10), pages 1442-1445.
    2. Tolley, George & Kenkel, Donald & Fabian, Robert (ed.), 1994. "Valuing Health for Policy," University of Chicago Press Economics Books, University of Chicago Press, edition 1, number 9780226807133, October.
    3. Kimberly M. Thompson & David E. Burmaster & Edmund A.C. Crouch3, 1992. "Monte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 12(1), pages 53-63, March.
    4. Holmberg, S.D., 1996. "The estimated prevalence and incidence of HIV in 96 large US metropolitan areas," American Journal of Public Health, American Public Health Association, vol. 86(5), pages 642-654.
    5. Giovanni Parmigiani & Greg P. Samsa & Marek Ancukiewicz & Joseph Lipscomb & Vic Hasselblad & David B. Matchar, 1997. "Assessing Uncertainty in Cost-Effectiveness Analyses," Medical Decision Making, , vol. 17(4), pages 390-401, October.
    6. Andrew Briggs & Paul Fenn, 1998. "Confidence intervals or surfaces? Uncertainty on the cost‐effectiveness plane," Health Economics, John Wiley & Sons, Ltd., vol. 7(8), pages 723-740, December.
    7. 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.
    8. 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.
    9. Ben A. Van Hout & Maiwenn J. Al & Gilad S. Gordon & Frans F. H. Rutten, 1994. "Costs, effects and C/E‐ratios alongside a clinical trial," Health Economics, John Wiley & Sons, Ltd., vol. 3(5), pages 309-319, September.
    10. Andrew H. Briggs, 1999. "A Bayesian approach to stochastic cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 257-261, May.
    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. Ana P. Johnson-Masotti & Purushottam W. Laud & Raymond G. Hoffmann & Matthew J. Hayat & Steven D. Pinkerton, 2001. "Probabilistic Cost-Effectiveness Analysis of HIV Prevention," Evaluation Review, , vol. 25(4), pages 474-502, August.
    2. A. E. Ades & G. Lu & K. Claxton, 2004. "Expected Value of Sample Information Calculations in Medical Decision Modeling," Medical Decision Making, , vol. 24(2), pages 207-227, March.
    3. 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.
    4. 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.
    5. Andrew Willan, 2011. "Sample Size Determination for Cost-Effectiveness Trials," PharmacoEconomics, Springer, vol. 29(11), pages 933-949, November.
    6. K. Claxton & P. J. Neumannn & S. S. Araki & M. C. Weinstein, "undated". "Bayesian Value-of-Information Analysis: An Application to a Policy Model of Alzheimer's Disease," Discussion Papers 00/39, Department of Economics, University of York.
    7. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost‐effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787, December.
    8. Anthony O’Hagan & John Stevens & Jacques Montmartin, 2000. "Inference for the Cost-Effectiveness Acceptability Curve and Cost-Effectiveness Ratio," PharmacoEconomics, Springer, vol. 17(4), pages 339-349, April.
    9. Simon Eckermann & Andrew R. Willan, 2009. "Globally optimal trial design for local decision making," Health Economics, John Wiley & Sons, Ltd., vol. 18(2), pages 203-216, February.
    10. 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.
    11. 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.
    12. 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.
    13. Karl Claxton & Elisabeth Fenwick & Mark J. Sculpher, 2012. "Decision-making with Uncertainty: The Value of Information," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 51, Edward Elgar Publishing.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Gafni, Amiram & Birch, Stephen, 2006. "Incremental cost-effectiveness ratios (ICERs): The silence of the lambda," Social Science & Medicine, Elsevier, vol. 62(9), pages 2091-2100, May.
    19. 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.
    20. 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.

    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:24:y:2004:i:6:p:634-653. 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.