IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v9y1989i1p14-25.html
   My bibliography  Save this article

Choosing Sample Sizes to Maximize Expected Health Benefits Subject to a Constraint on Total Trial Costs

Author

Listed:
  • Stuart G. Baker
  • Kurt Heidenberger

Abstract

The authors present a method for choosing sample sizes for randomized controlled trials that maximizes expected health benefits (measured in expected discounted life years gained) subject to the decision maker's budget constraint. In comparison with similar approaches, the method introduces richer and more realistic models for the following quantities: costs and benefits during and after the trial, rates of adopting interventions after a positive rec ommendation, the distribution of data collected in the trial, and the decision to make a positive recommendation based on the results of the trial. Although the methodology is applicable to any type of trial, the emphasis in the paper is on prevention trials. Calculations involve Monte Carlo methods. An example is provided.

Suggested Citation

  • Stuart G. Baker & Kurt Heidenberger, 1989. "Choosing Sample Sizes to Maximize Expected Health Benefits Subject to a Constraint on Total Trial Costs," Medical Decision Making, , vol. 9(1), pages 14-25, February.
    • Handle: RePEc:sae:medema:v:9:y:1989:i:1:p:14-25
    DOI: 10.1177/0272989X8900900104
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X8900900104
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X8900900104?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. Frederick Mosteller & Milton Weinstein, 1985. "Toward Evaluating the Cost-Effectiveness of Medical and Social Experiments," NBER Chapters, in: Social Experimentation, pages 221-250, National Bureau of Economic Research, Inc.
    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. Marion S. Rauner & Walter J. Gutjahr & Kurt Heidenberger & Joachim Wagner & Joseph Pasia, 2010. "Dynamic Policy Modeling for Chronic Diseases: Metaheuristic-Based Identification of Pareto-Optimal Screening Strategies," Operations Research, INFORMS, vol. 58(5), pages 1269-1286, October.

    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.

      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:9:y:1989:i:1:p:14-25. 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.