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Lessons from Trial-Based Cost-Effectiveness Analyses of Mental Health Interventions

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  • Jeffrey Hoch
  • Carolyn Dewa

Abstract

The principal aim of this article is to share lessons learned by the authors while conducting economic evaluations, using clinical trial data, of mental health interventions. These lessons are quite general and have clear relevance for pharmacoeconomic studies. In addition, we explore how net benefit regression can be used to enhance consideration of key issues when conducting an economic evaluation based on clinical trial data. The first study we discuss found that cost-effectiveness results varied markedly based on the choice of both the patient outcome and the willingness to pay for more of that outcome. The importance of willingness to pay was also highlighted in the results from the second study. Even with a set willingness-to-pay value, most of the time the probability that the new treatment was cost effective was not 100%. In the third study, the cost effectiveness of the new treatment varied by patient characteristics. These observations have important implications for pharmacoeconomic studies. Namely, analysts must carefully consider choice of patient outcome, willingness to pay, patient heterogeneity and the statistical uncertainty inherent in the data. Net benefit regression is a useful technique for exploring these crucial issues when undertaking an economic evaluation using patient-level data on both costs and effects. Copyright Adis Data Information BV 2007

Suggested Citation

  • Jeffrey Hoch & Carolyn Dewa, 2007. "Lessons from Trial-Based Cost-Effectiveness Analyses of Mental Health Interventions," PharmacoEconomics, Springer, vol. 25(10), pages 807-816, October.
  • Handle: RePEc:spr:pharme:v:25:y:2007:i:10:p:807-816
    DOI: 10.2165/00019053-200725100-00001
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    References listed on IDEAS

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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, May.
    2. Drummond, Michael F. & Sculpher, Mark J. & Torrance, George W. & O'Brien, Bernie J. & Stoddart, Greg L., 2005. "Methods for the Economic Evaluation of Health Care Programmes," OUP Catalogue, Oxford University Press, edition 3, number 9780198529453.
    3. Tambour, Magnus & Zethraeus, Niklas & Johannesson, Magnus, 1997. "A Note on Confidence Intervals in Cost-Effectiveness Analysis," SSE/EFI Working Paper Series in Economics and Finance 181, Stockholm School of Economics.
    4. 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.
    5. Anirban Basu & Bhakti V. Arondekar & Paul J. Rathouz, 2006. "Scale of interest versus scale of estimation: comparing alternative estimators for the incremental costs of a comorbidity," Health Economics, John Wiley & Sons, Ltd., vol. 15(10), pages 1091-1107, October.
    6. 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.
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