IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v22y2002i3p245-261.html
   My bibliography  Save this article

Using Elicitation Techniques to Estimate the Value of Ambulatory Treatments for Major Depression

Author

Listed:
  • Sharon-Lise T. Normand

    (Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts)

  • Richard G. Frank

    (Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts)

  • Thomas G. McGuire

    (Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts)

Abstract

Estimating the value of spending on medical treatments in a health care system involves relating output, measured in terms of effectiveness, to cost, measured in terms of spending. Although information on spending at the system level often exists in administrative data, such as insurance claims, information on effectiveness is not always available. An inferential tool available to researchers in this context is elicitation. The authors develop an approach to elicit effectiveness parameters and apply it to a panel of 10 experts to estimate predictive Hamilton Depression Rating Scale scores representing postambulatory treatment outcomes. The elicited parameters are used to estimate outcomes associated with 120 acute phase treatments for major depression within a privately insured health insurance system. The outcome-adjusted price per full remission episode is estimated for each acute treatment, and corresponding 95% percentile bootstrap intervals are calculated. The average spending for all observed treatments was $473 (SE = 478), with a depression-free adjusted price per case of $5995 (95% confidence interval = $5959-$6031).

Suggested Citation

  • Sharon-Lise T. Normand & Richard G. Frank & Thomas G. McGuire, 2002. "Using Elicitation Techniques to Estimate the Value of Ambulatory Treatments for Major Depression," Medical Decision Making, , vol. 22(3), pages 245-261, June.
  • Handle: RePEc:sae:medema:v:22:y:2002:i:3:p:245-261
    DOI: 10.1177/0272989X0202200313
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X0202200313
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X0202200313?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 L. Winkler, 1968. "The Consensus of Subjective Probability Distributions," Management Science, INFORMS, vol. 15(2), pages 61-75, October.
    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. Maria Orlando Edelen & M. Audrey Burnam & Katherine E. Watkins & José J. Escarce & Haiden Huskamp & Howard H. Goldman & Gary Rachelefsky, 2008. "Obtaining Utility Estimates of the Health Value of Commonly Prescribed Treatments for Asthma and Depression," Medical Decision Making, , vol. 28(5), pages 732-750, September.
    2. Richard G. Frank & Ernst R. Berndt & Alisa B. Busch, 2003. "Quality-Constant Price Indexes for the Ongoing Treatment of Schizophrenia: An Exploratory Study," NBER Working Papers 10022, National Bureau of Economic Research, Inc.

    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. Joseph Kadane & Javier Girón & Daniel Peña & Peter Fishburn & Simon French & D. Lindley & Giovanni Parmigiani & Robert Winkler, 1993. "Several Bayesians: A review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 2(1), pages 1-32, December.
    2. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    3. Hanea, D.M. & Jagtman, H.M. & van Alphen, L.L.M.M. & Ale, B.J.M., 2010. "Quantitative and qualitative analysis of the expert and non-expert opinion in fire risk in buildings," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 729-741.
    4. Christian Kascha & Francesco Ravazzolo, 2010. "Combining inflation density forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 231-250.
    5. Mohammad J. Abdolmohammadi & Paul D. Berger, 1986. "A test of the accuracy of probability assessment techniques in auditing," Contemporary Accounting Research, John Wiley & Sons, vol. 3(1), pages 149-165, September.
    6. David M. Pennock & Michael P. Wellman, 2005. "Graphical Models for Groups: Belief Aggregation and Risk Sharing," Decision Analysis, INFORMS, vol. 2(3), pages 148-164, September.
    7. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    8. Hoi K. Suen, 1984. "A Procedure for the Derivation of the Best Estimate When Empirical Data Are Unattainable," Evaluation Review, , vol. 8(5), pages 734-743, October.
    9. Mohammad J. Abdolmohammadi & Paul D. Berger, 1986. "Une expérience sur la précision des techniques d‘évaluation des probabilités en vérification," Contemporary Accounting Research, John Wiley & Sons, vol. 3(1), pages 166-183, September.
    10. Tommaso Proietti & Martyna Marczak & Gianluigi Mazzi, 2017. "Euromind‐ D : A Density Estimate of Monthly Gross Domestic Product for the Euro Area," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 683-703, April.
    11. Marcello Basili & Luca Pratelli, 2013. "Aggregation of not necessarily independent opinions," Department of Economics University of Siena 677, Department of Economics, University of Siena.
    12. Anderson, Jock R. & Hardaker, J. Brian, 1972. "An Appreciation of Decision Analysis in Management," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 40(04), pages 1-15, December.
    13. Hurley, W. J. & Lior, D. U., 2002. "Combining expert judgment: On the performance of trimmed mean vote aggregation procedures in the presence of strategic voting," European Journal of Operational Research, Elsevier, vol. 140(1), pages 142-147, July.
    14. Cho, Sungbin, 2009. "A linear Bayesian stochastic approximation to update project duration estimates," European Journal of Operational Research, Elsevier, vol. 196(2), pages 585-593, July.
    15. Greig, I.D., 1981. "Agricultural Research Management and the Ex Ante Evaluation of Research Proposals : A Review," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 49(02), pages 1-22, August.
    16. Timo Henckel & Shaun Vahey & Liz Wakerly, 2011. "Probabilistic interest rate setting with a shadow board: A description of the pilot project," CAMA Working Papers 2011-27, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    17. Li, Yongquan & Zhu, Kaijie, 2009. "Information acquisition in new product introduction," European Journal of Operational Research, Elsevier, vol. 198(2), pages 618-625, October.
    18. Eggstaff, Justin W. & Mazzuchi, Thomas A. & Sarkani, Shahram, 2014. "The effect of the number of seed variables on the performance of Cooke′s classical model," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 72-82.
    19. Fu, Qi & Zhu, Kaijie, 2010. "Endogenous information acquisition in supply chain management," European Journal of Operational Research, Elsevier, vol. 201(2), pages 454-462, March.
    20. Jen Tang & Kwei Tang & Herbert Moskowitz, 1997. "Exact bayesian estimation of system reliability from component test data," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(1), pages 127-146, February.

    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:22:y:2002:i:3:p:245-261. 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.