IDEAS home Printed from https://ideas.repec.org/a/spr/pharme/v27y2009i12p1017-1029.html
   My bibliography  Save this article

Bayesian Modelling of Healthcare Resource Use in Multinational Randomized Clinical Trials

Author

Listed:
  • Aline Gauthier
  • Andrea Manca
  • Susan Anton

Abstract

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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.2165/11314030-000000000-00000
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.2165/11314030-000000000-00000?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
    ---><---

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

    References listed on IDEAS

    as
    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 & John Cairns, 2007. "Multilevel models for estimating incremental net benefits in multinational studies," Health Economics, John Wiley & Sons, Ltd., vol. 16(8), pages 815-826, August.
    3. Sarah Wordsworth & Anne Ludbrook, 2005. "Comparing costing results in across country economic evaluations: the use of technology specific purchasing power parities," Health Economics, John Wiley & Sons, Ltd., vol. 14(1), pages 93-99, January.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    5. 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.
    6. 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.
    7. Glick, Henry A & Doshi, Jalpa A & Sonnad, Seema S & Polsky, Daniel, 2007. "Economic Evaluation in Clinical Trials," OUP Catalogue, Oxford University Press, number 9780198529972, Decembrie.
    8. 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.
    9. Richard M. Nixon & Simon G. Thompson, 2005. "Methods for incorporating covariate adjustment, subgroup analysis and between‐centre differences into cost‐effectiveness evaluations," Health Economics, John Wiley & Sons, Ltd., vol. 14(12), pages 1217-1229, December.
    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. 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.

    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. Richard Grieve & John Cairns & Simon G. Thompson, 2010. "Improving costing methods in multicentre economic evaluation: the use of multiple imputation for unit costs," Health Economics, John Wiley & Sons, Ltd., vol. 19(8), pages 939-954, August.
    2. Manuel Gomes & Richard Grieve & Richard Nixon & W. J. Edmunds, 2012. "Statistical Methods for Cost-Effectiveness Analyses That Use Data from Cluster Randomized Trials," Medical Decision Making, , vol. 32(1), pages 209-220, January.
    3. Mohamed El Alili & Johanna M. van Dongen & Jonas L. Esser & Martijn W. Heymans & Maurits W. van Tulder & Judith E. Bosmans, 2022. "A scoping review of statistical methods for trial‐based economic evaluations: The current state of play," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2680-2699, December.
    4. Christian E. H. Boehler & Joanne Lord, 2016. "Mind the Gap! A Multilevel Analysis of Factors Related to Variation in Published Cost-Effectiveness Estimates within and between Countries," Medical Decision Making, , vol. 36(1), pages 31-47, January.
    5. Moreno, Elías & Girón, F.J. & Vázquez-Polo, F.J. & Negrín, M.A., 2012. "Optimal healthcare decisions: The importance of the covariates in cost–effectiveness analysis," European Journal of Operational Research, Elsevier, vol. 218(2), pages 512-522.
    6. Hege Urdahl & Andrea Manca & Mark Sculpher, 2006. "Assessing Generalisability in Model-Based Economic Evaluation Studies," PharmacoEconomics, Springer, vol. 24(12), pages 1181-1197, December.
    7. 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.
    8. Andrew R. Willan & Matthew E. Kowgier, 2008. "Cost‐effectiveness analysis of a multinational RCT with a binary measure of effectiveness and an interacting covariate," Health Economics, John Wiley & Sons, Ltd., vol. 17(7), pages 777-791, July.
    9. Manuel Gomes & Karla Díaz-Ordaz & Richard Grieve & Michael G. Kenward, 2013. "Multiple Imputation Methods for Handling Missing Data in Cost-effectiveness Analyses That Use Data from Hierarchical Studies," Medical Decision Making, , vol. 33(8), pages 1051-1063, November.
    10. Alexina J. Mason & Manuel Gomes & James Carpenter & Richard Grieve, 2021. "Flexible Bayesian longitudinal models for cost‐effectiveness analyses with informative missing data," Health Economics, John Wiley & Sons, Ltd., vol. 30(12), pages 3138-3158, December.
    11. Raymond Oppong & Sue Jowett & Tracy E Roberts, 2015. "Economic Evaluation alongside Multinational Studies: A Systematic Review of Empirical Studies," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-22, June.
    12. Manuel Gomes & Richard Grieve & Richard Nixon & Edmond S.‐W. Ng & James Carpenter & Simon G. Thompson, 2012. "Methods For Covariate Adjustment In Cost‐Effectiveness Analysis That Use Cluster Randomised Trials," Health Economics, John Wiley & Sons, Ltd., vol. 21(9), pages 1101-1118, September.
    13. Noémi Kreif & Richard Grieve & M. Zia Sadique, 2013. "Statistical Methods For Cost‐Effectiveness Analyses That Use Observational Data: A Critical Appraisal Tool And Review Of Current Practice," Health Economics, John Wiley & Sons, Ltd., vol. 22(4), pages 486-500, April.
    14. Pepijn Vemer & Maureen Rutten-van Mölken, 2013. "The Road Not Taken: Transferability Issues in Multinational Trials," PharmacoEconomics, Springer, vol. 31(10), pages 863-876, October.
    15. Deidda, Manuela & Geue, Claudia & Kreif, Noemi & Dundas, Ruth & McIntosh, Emma, 2019. "A framework for conducting economic evaluations alongside natural experiments," Social Science & Medicine, Elsevier, vol. 220(C), pages 353-361.
    16. David Epstein & Anne Mason & Andrea Manca, 2008. "The hospital costs of care for stroke in nine European countries," Health Economics, John Wiley & Sons, Ltd., vol. 17(S1), pages 21-31, January.
    17. Richard Grieve & Richard Nixon & Simon G. Thompson & John Cairns, 2007. "Multilevel models for estimating incremental net benefits in multinational studies," Health Economics, John Wiley & Sons, Ltd., vol. 16(8), pages 815-826, August.
    18. Jonas Schreyögg, 2008. "A micro‐costing approach to estimating hospital costs for appendectomy in a Cross‐European context," Health Economics, John Wiley & Sons, Ltd., vol. 17(S1), pages 59-69, January.
    19. Andrea Gabrio & Michael J. Daniels & Gianluca Baio, 2020. "A Bayesian parametric approach to handle missing longitudinal outcome data in trial‐based health economic evaluations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 607-629, February.
    20. Gomes, M & Grieve, R, 2011. "Estimating the Effects of Friendship Networks on Health Behaviors of Adolescents," Health, Econometrics and Data Group (HEDG) Working Papers 11/14, HEDG, c/o Department of Economics, University of York.

    More about this item

    Statistics

    Access and download statistics

    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:spr:pharme:v:27:y:2009:i:12:p:1017-1029. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.