IDEAS home Printed from
   My bibliography  Save this article

Bayesian Modelling of Healthcare Resource Use in Multinational Randomized Clinical Trials


  • Aline Gauthier


  • Andrea Manca
  • Susan Anton


Background: Most cost-effectiveness analyses conducted alongside multinational randomized clinical trials (RCT) are carried out applying the unit costs from the country of interest to trial-wide resource use items with the objective of estimating total healthcare costs by treatment group. However, this approach could confound ‘price effects’ with ‘country effects’. An alternative approach is to use multilevel modelling techniques to analyse healthcare resource use (HCRU) from the trial, and obtain country-specific total costs by applying country-specific unit costs to corresponding shrinkage estimates of differential HCRU. Methods: To illustrate the feasibility of this approach, we analysed data from twin multinational RCTs, which enrolled approximately 2000 individuals into three treatment arms for the management of patients with chronic respiratory disease. The models were implemented using Bayesian multilevel models, to reflect the hierarchical structure of the data while controlling for co-variates at the patient and country level. Results: This analysis showed that directly modelling the level of HCRU is a promising approach to facilitate cost-effectiveness analyses conducted alongside multinational RCTs, offering several advantages compared with the modelling of direct costs. Conclusions: It is argued that modelling the level of HCRU within the Bayesian framework avoids confounding the price effects with the country effects and facilitates the estimation of costs for several countries represented in the trial. Copyright Adis Data Information BV 2009

Suggested Citation

  • Aline Gauthier & Andrea Manca & Susan Anton, 2009. "Bayesian Modelling of Healthcare Resource Use in Multinational Randomized Clinical Trials," PharmacoEconomics, Springer, vol. 27(12), pages 1017-1029, December.
  • Handle: RePEc:spr:pharme:v:27:y:2009:i:12:p:1017-1029
    DOI: 10.2165/11314030-000000000-00000

    Download full text from publisher

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Andrea Manca & Nigel Rice & Mark J. Sculpher & Andrew H. Briggs, 2005. "Assessing generalisability by location in trial‐based cost‐effectiveness analysis: the use of multilevel models," Health Economics, John Wiley & Sons, Ltd., vol. 14(5), pages 471-485, May.
    2. Richard Grieve & Richard Nixon & Simon G. Thompson & Charles Normand, 2005. "Using multilevel models for assessing the variability of multinational resource use and cost data," Health Economics, John Wiley & Sons, Ltd., vol. 14(2), pages 185-196, February.
    3. Thompson, Simon G. & Nixon, Richard M. & Grieve, Richard, 2006. "Addressing the issues that arise in analysing multicentre cost data, with application to a multinational study," Journal of Health Economics, Elsevier, vol. 25(6), pages 1015-1028, November.
    4. Glick, Henry A & Doshi, Jalpa A & Sonnad, Seema S & Polsky, Daniel, 2007. "Economic Evaluation in Clinical Trials," OUP Catalogue, Oxford University Press, number 9780198529972.
    5. Paul C. Lambert & Lucinda J. Billingham & Nicola J. Cooper & Alex J. Sutton & Keith R. Abrams, 2008. "Estimating the cost‐effectiveness of an intervention in a clinical trial when partial cost information is available: a Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 67-81, January.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Anna-Liesa Lange & Philipp Otto, 2016. "Bayes’sche Statistik in der Dienstleistungsforschung [Bayesian statistics in service research]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(4), pages 247-267, December.

    More about this item


    Access and download statistics


    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:spr:pharme:v:27:y:2009:i:12:p:1017-1029. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Springer Nature Abstracting and Indexing). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.