IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v17y1997i4p409-426.html
   My bibliography  Save this article

A Normative Analytic Framework for Development of Practice Guidelines for Specific Clinical Populations

Author

Listed:
  • Douglas K. Owens
  • Robert F. Nease Jr.

Abstract

Background. A central problem in practice guideline development is how to develop guidelines that appropriately account for variations in clinical populations and practice settings. Despite recognition of this problem, there is no formal mechanism for as sessing what the need is for flexibility in guidelines, or for deciding how to incorporate such flexibility into recommendations. Objective. This research sought to provide a formal basis to determine when clinical circumstances vary sufficiently that guideline recommendations should differ, how recommendations should be tailored for a specific clinical setting, and whether the benefit associated with such site-specific guidelines justifies the expense of their development. Results. The authors describe an approach for estimating the maximum health benefit that developers can obtain by eliminating uncertainty about differences in the patient populations and practice settings in which a guideline will be used. This estimate, the expected value of customization, provides a mechanism to evaluate the cost-effectiveness of the development of site-specific guidelines that account explicitly for variation in clinical circumstances. Application of this method to the development of screening guidelines for human immunodeficiency virus (HIV) infection indicates that the development of site-specific guidelines poten tially is cost-effective. Site-specific guidelines either improve, or leave unchanged, the efficiency of HIV screening; whether they increase or decrease total expenditures and health benefits depends on the choice of a cost-effectiveness threshold, and the clin ical problem. Conclusions. Development of guideline recommendations based on de cision models provides a normative approach for evaluating the need for and the cost-effectiveness of site-specific guidelines that have been tailored to specific prac tice settings. Such site-specific guidelines can improve substantially the expected health benefit and the economic efficiency of practice guidelines. Key words: practice guidelines; decision models; cost-effectiveness; site-specific guidelines. (Med Decis Making 1997;17:409-426)

Suggested Citation

  • Douglas K. Owens & Robert F. Nease Jr., 1997. "A Normative Analytic Framework for Development of Practice Guidelines for Specific Clinical Populations," Medical Decision Making, , vol. 17(4), pages 409-426, October.
  • Handle: RePEc:sae:medema:v:17:y:1997:i:4:p:409-426
    DOI: 10.1177/0272989X9701700406
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X9701700406
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X9701700406?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. Ronald A. Howard, 1988. "Decision Analysis: Practice and Promise," Management Science, INFORMS, vol. 34(6), pages 679-695, June.
    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. Mark J. Sculpher & Karl Claxton & Mike Drummond & Chris McCabe, 2006. "Whither trial‐based economic evaluation for health care decision making?," Health Economics, John Wiley & Sons, Ltd., vol. 15(7), pages 677-687, July.

    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. Alan Brennan & Samer Kharroubi & Anthony O'Hagan & Jim Chilcott, 2007. "Calculating Partial Expected Value of Perfect Information via Monte Carlo Sampling Algorithms," Medical Decision Making, , vol. 27(4), pages 448-470, July.
    2. Thomas W. Keelin & Bradford W. Powley, 2011. "Quantile-Parameterized Distributions," Decision Analysis, INFORMS, vol. 8(3), pages 206-219, September.
    3. David V. Pynadath & Bistra Dilkina & David C. Jeong & Richard S. John & Stacy C. Marsella & Chirag Merchant & Lynn C. Miller & Stephen J. Read, 2023. "Disaster world," Computational and Mathematical Organization Theory, Springer, vol. 29(1), pages 84-117, March.
    4. Caetani, Alberto Pavlick & Ferreira, Luciano & Borenstein, Denis, 2016. "Development of an integrated decision-making method for an oil refinery restructuring in Brazil," Energy, Elsevier, vol. 111(C), pages 197-210.
    5. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    6. Craig R. Fox & Robert T. Clemen, 2005. "Subjective Probability Assessment in Decision Analysis: Partition Dependence and Bias Toward the Ignorance Prior," Management Science, INFORMS, vol. 51(9), pages 1417-1432, September.
    7. Dimitrios Gouglas & Kendall Hoyt & Elizabeth Peacocke & Aristidis Kaloudis & Trygve Ottersen & John-Arne Røttingen, 2019. "Setting Strategic Objectives for the Coalition for Epidemic Preparedness Innovations: An Exploratory Decision Analysis Process," Service Science, INFORMS, vol. 49(6), pages 430-446, November.
    8. Ali E. Abbas & Il-Horn Hann, 2010. "Measuring Risk Aversion in a Name-Your-Own-Price Channel," Decision Analysis, INFORMS, vol. 7(1), pages 123-136, March.
    9. Roberto Ley-Borrás, 2015. "Deciding on the Decision Situation to Analyze: The Critical First Step of a Decision Analysis," Decision Analysis, INFORMS, vol. 12(1), pages 46-58, March.
    10. Siebert, Johannes Ulrich & Kunz, Reinhard E. & Rolf, Philipp, 2021. "Effects of decision training on individuals’ decision-making proactivity," European Journal of Operational Research, Elsevier, vol. 294(1), pages 264-282.
    11. Manel Baucells & Samuel E. Bodily, 2024. "The Discount Rate for Investment Analysis Applying Expected Utility," Decision Analysis, INFORMS, vol. 21(2), pages 125-141, June.
    12. H Xiong & J Xie & X Deng, 2011. "Risk-averse decision making in overbooking problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1655-1665, September.
    13. Borgonovo, Emanuele & Tonoli, Fabio, 2014. "Decision-network polynomials and the sensitivity of decision-support models," European Journal of Operational Research, Elsevier, vol. 239(2), pages 490-503.
    14. C-C Chang & R-S Chen, 2007. "Project advancement and its applications to multi-air-route quality budget allocation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(8), pages 1008-1020, August.
    15. Julie Rozenberg & Céline Guivarch & Robert Lempert & Stéphane Hallegatte, 2014. "Building SSPs for climate policy analysis: a scenario elicitation methodology to map the space of possible future challenges to mitigation and adaptation," Climatic Change, Springer, vol. 122(3), pages 509-522, February.
    16. Elena Verdolini & Laura Díaz Anadón & Erin Baker & Valentina Bosetti & Lara Aleluia Reis, 2018. "Future Prospects for Energy Technologies: Insights from Expert Elicitations," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 12(1), pages 133-153.
    17. A Morton & L D Phillips, 2009. "Fifty years of probabilistic decision analysis: a view from the UK," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 33-40, May.
    18. Laure Cabantous & Jean-Pascal Gond, 2011. "Rational Decision Making as Performative Praxis: Explaining Rationality's Éternel Retour," Organization Science, INFORMS, vol. 22(3), pages 573-586, June.
    19. Xiao, Xiao & Seekamp, Erin & van der Burg, Max Post & Eaton, Mitchell & Fatorić, Sandra & McCreary, Allie, 2019. "Optimizing historic preservation under climate change: Decision support for cultural resource adaptation planning in national parks," Land Use Policy, Elsevier, vol. 83(C), pages 379-389.
    20. Craig W. Kirkwood, 2004. "Approximating Risk Aversion in Decision Analysis Applications," Decision Analysis, INFORMS, vol. 1(1), pages 51-67, March.

    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:17:y:1997:i:4:p:409-426. 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.