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Cost-Effectiveness Analysis Using Data from Multinational Trials: The Use of Bivariate Hierarchical Modeling

Author

Listed:
  • Andrea Manca

    (Centre for Health Economics, University of York, UK, am126@york.ac.uk)

  • Paul C. Lambert

    (Centre for Biostatistics & Genetic Epidemiology, Department of Health Sciences, University of Leicester, UK)

  • Mark Sculpher

    (Centre for Health Economics, University of York, UK)

  • Nigel Rice

    (Centre for Health Economics, University of York, UK)

Abstract

Health care cost-effectiveness analysis (CEA) often uses individual patient data (IPD) from multinational randomized controlled trials. Although designed to account for between-patient sampling variability in the clinical and economic data, standard analytical approaches to CEA ignore the presence of between-location variability in the study results. This is a restrictive limitation given that countries often differ in factors that could affect the results of CEAs, such as the availability of health care resources, their unit costs, clinical practice, and patient case mix. The authors advocate the use of Bayesian bivariate hierarchical modeling to analyze multinational cost-effectiveness data. This analytical framework explicitly recognizes that patient-level costs and outcomes are nested within countries. Using real-life data, the authors illustrate how the proposed methods can be applied to obtain (a) more appropriate estimates of overall cost-effectiveness and associated measure of sampling uncertainty compared to standard CEA and (b) country-specific cost-effectiveness estimates that can be used to assess the between-location variability of the study results while controlling for differences in country-specific and patientspecific characteristics. It is demonstrated that results from standard CEA using IPD from multinational trials display a large degree of variability across the 17 countries included in the analysis, producing potentially misleading results. In contrast, ``shrinkage estimates'' obtained from the modeling approach proposed here facilitate the appropriate quantification of country-specific cost-effectiveness estimates while weighting the results based on the level of information available within each country. The authors suggest that the methods presented here represent a general framework for the analysis of economic data collected from different locations.

Suggested Citation

  • Andrea Manca & Paul C. Lambert & Mark Sculpher & Nigel Rice, 2007. "Cost-Effectiveness Analysis Using Data from Multinational Trials: The Use of Bivariate Hierarchical Modeling," Medical Decision Making, , vol. 27(4), pages 471-490, July.
  • Handle: RePEc:sae:medema:v:27:y:2007:i:4:p:471-490
    DOI: 10.1177/0272989X07302132
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    References listed on IDEAS

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    1. Andrew R. Willan & Andrew H. Briggs & Jeffrey S. Hoch, 2004. "Regression methods for covariate adjustment and subgroup analysis for non‐censored cost‐effectiveness data," Health Economics, John Wiley & Sons, Ltd., vol. 13(5), pages 461-475, May.
    2. Jeffrey S. Hoch & Andrew H. Briggs & Andrew R. Willan, 2002. "Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 11(5), pages 415-430, July.
    3. Andrew R. Willan & Eleanor M. Pinto & Bernie J. O'Brien & Padma Kaul & Ron Goeree & Larry Lynd & Paul W. Armstrong, 2005. "Country specific cost comparisons from multinational clinical trials using empirical Bayesian shrinkage estimation: the Canadian ASSENT‐3 economic analysis," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 327-338, April.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Adrian Gheorghe & Tracy E Roberts & Jonathan C Ives & Benjamin R Fletcher & Melanie Calvert, 2013. "Centre Selection for Clinical Trials and the Generalisability of Results: A Mixed Methods Study," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-9, February.
    4. Adrian Gheorghe & Tracy Roberts & Thomas D. Pinkney & Dion G. Morton & Melanie Calvert, 2015. "Rational Centre Selection for RCTs with a Parallel Economic Evaluation—the Next Step Towards Increased Generalisability?," Health Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 498-504, April.
    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. Ângela Jornada Ben & Johanna M. Dongen & Mohamed El Alili & Martijn W. Heymans & Jos W. R. Twisk & Janet L. MacNeil-Vroomen & Maartje Wit & Susan E. M. Dijk & Teddy Oosterhuis & Judith E. Bosmans, 2023. "The handling of missing data in trial-based economic evaluations: should data be multiply imputed prior to longitudinal linear mixed-model analyses?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(6), pages 951-965, August.

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