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Cost-effectiveness analysis of a multinational RCT with a binary measure of effectiveness and an interacting covariate

  • Andrew R. Willan
  • Matthew E. Kowgier

    (Department of Public Health Sciences, University of Toronto, Toronto, Ont., Canada)

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    In a recent multinational randomized clinical trial, 1356 patients from 14 countries were randomized between two arms. The primary measure of effectiveness was 30-day survival. Health care utilization was collected on all patients and was combined with a single country's price weights to provide patient-level cost data. The purpose of this paper is to report the results of the cost-effectiveness analysis for the country that provided the cost weights, so as to provide a case study for illustrating recently proposed methodologies that account for skewed cost data, the between-country variation in treatment effects, possible interactions between treatment and baseline covariates, and the difficulty of estimated adjusted risk differences. A hierarchal model is used to account for the two sources of variation (between country and between patients, within a country). The model, which uses gamma distributions for cost data and recent methods for estimating adjusted risk differences, provides overall and country-specific estimates of treatment effects. Model estimation is facilitated by Markov chain Monte Carlo methods using the WinBUGS software. In addition, the theory of expected value of information is used to determine if the data provided by the trial are sufficient for decision making. Copyright © 2007 John Wiley & Sons, Ltd.

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    Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

    Volume (Year): 17 (2008)
    Issue (Month): 7 ()
    Pages: 777-791

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    Handle: RePEc:wly:hlthec:v:17:y:2008:i:7:p:777-791
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    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.
    2. 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.
    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.
    4. Richard J. Willke & Henry A. Glick & Daniel Polsky & Kevin Schulman, 1998. "Estimating country-specific cost-effectiveness from multinational clinical trials," Health Economics, John Wiley & Sons, Ltd., vol. 7(6), pages 481-493.
    5. Anthony O'Hagan & John W. Stevens, 2003. "Assessing and comparing costs: how robust are the bootstrap and methods based on asymptotic normality?," Health Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 33-49.
    6. Karl Claxton & John Posnett, . "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
    7. 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.
    8. Elisabeth Fenwick & Bernie J. O'Brien & Andrew Briggs, 2004. "Cost-effectiveness acceptability curves - facts, fallacies and frequently asked questions," Health Economics, John Wiley & Sons, Ltd., vol. 13(5), pages 405-415.
    9. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
    10. Claxton, K. & Thompson, K. M., 2001. "A dynamic programming approach to the efficient design of clinical trials," Journal of Health Economics, Elsevier, vol. 20(5), pages 797-822, September.
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