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Optimal treatments in cost-effectiveness analysis in the presence of covariates: Improving patient subgroup definition

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  • Moreno, E.
  • Girón, F.J.
  • Martínez, M.L.
  • Vázquez-Polo, F.J.
  • Negrín, M.A.
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    Abstract

    In the presence of covariates, the cost-effectiveness analysis of medical treatments shows that the optimal treatment varies across the patient population subgroups, and hence to accurately define the subgroups is a crucial step in the analysis. A patient subgroup definition using only influential covariates within the potential set of patients covariates established by the expert has recently been proposed, and the influential covariates were chosen from the univariate distributions of the effectiveness and the cost, conditional on the effectiveness. In this paper, we argue that the Bayesian variable selection procedure should be developed using the bivariate distribution of the cost and the effectiveness, which is not the usual practice. This new approach, provides results with wider applicability and more understandable without a significative increase in the complexity of the procedure. For real and simulated data sets, optimal treatments for subgroups are found, and compared with that from previous methods.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 226 (2013)
    Issue (Month): 1 ()
    Pages: 173-182

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    Handle: RePEc:eee:ejores:v:226:y:2013:i:1:p:173-182

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Bayesian variable selection; Cost-effectiveness analysis; Bivariate regression model; Intrinsic priors; Subgroup analysis; Utility function;

    References

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    1. Mark Sculpher & Amiram Gafni, 2001. "Recognizing diversity in public preferences: The use of preference sub-groups in cost-effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 10(4), pages 317-324.
    2. Maiwenn J. Al & Ben A. Van Hout, 2000. "A Bayesian approach to economic analyses of clinical trials: the case of stenting versus balloon angioplasty," Health Economics, John Wiley & Sons, Ltd., vol. 9(7), pages 599-609.
    3. Mark Sculpher, 2008. "Subgroups and Heterogeneity in Cost-Effectiveness Analysis," PharmacoEconomics, Springer Healthcare | Adis, vol. 26(9), pages 799-806.
    4. Elías Moreno & F. Girón, 2008. "Comparison of Bayesian objective procedures for variable selection in linear regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 17(3), pages 491-492, November.
    5. Francisco-José Polo & Miguel Negrín & Xavier Badía & Montse Roset, 2005. "Bayesian regression models for cost-effectiveness analysis," The European Journal of Health Economics, Springer, vol. 6(1), pages 45-52, March.
    6. Gerald Richardson & Andrea Manca, 2004. "Calculation of quality adjusted life years in the published literature: a review of methodology and transparency," Health Economics, John Wiley & Sons, Ltd., vol. 13(12), pages 1203-1210.
    7. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    8. Aaron A. Stinnett & John Mullahy, 1998. "Net Health Benefits: A New Framework for the Analysis of Uncertainty in Cost-Effectiveness Analysis," NBER Technical Working Papers 0227, National Bureau of Economic Research, Inc.
    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.
    10. 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.
    11. 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.
    12. Casella, George & Moreno, Elías, 2009. "Assessing Robustness of Intrinsic Tests of Independence in Two-Way Contingency Tables," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1261-1271.
    13. 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.
    14. Brailsford, Sally & Harper, Paul, 2008. "OR in Health," European Journal of Operational Research, Elsevier, vol. 185(3), pages 901-904, March.
    15. Moreno, Elías & Girón, F.J. & Vázquez-Polo, F.J. & NegrI´n, M.A., 2010. "Optimal healthcare decisions: Comparing medical treatments on a cost-effectiveness basis," European Journal of Operational Research, Elsevier, vol. 204(1), pages 180-187, July.
    16. F. J. Vázquez-Polo & M. A. Negr�n Hernández & B. González López-Valcárcel, 2005. "Using covariates to reduce uncertainty in the economic evaluation of clinical trial data," Health Economics, John Wiley & Sons, Ltd., vol. 14(6), pages 545-557.
    17. Elías Moreno & F. Girón, 2008. "Comparison of Bayesian objective procedures for variable selection in linear regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 17(3), pages 472-490, November.
    18. Andrea Manca & Mark J. Sculpher & Ron Goeree, 2010. "The Analysis of Multinational Cost-Effectiveness Data for Reimbursement Decisions: A Critical Appraisal of Recent Methodological Developments," PharmacoEconomics, Springer Healthcare | Adis, vol. 28(12), pages 1079-1096.
    19. Casella, George & Moreno, Elias, 2006. "Objective Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 157-167, March.
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