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Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost‐effectiveness analysis

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  • Jeffrey S. Hoch
  • Andrew H. Briggs
  • Andrew R. Willan

Abstract

Economic evaluation is often seen as a branch of health economics divorced from mainstream econometric techniques. Instead, it is perceived as relying on statistical methods for clinical trials. Furthermore, the statistic of interest in cost‐effectiveness analysis, the incremental cost‐effectiveness ratio is not amenable to regression‐based methods, hence the traditional reliance on comparing aggregate measures across the arms of a clinical trial. In this paper, we explore the potential for health economists undertaking cost‐effectiveness analysis to exploit the plethora of established econometric techniques through the use of the net‐benefit framework – a recently suggested reformulation of the cost‐effectiveness problem that avoids the reliance on cost‐effectiveness ratios and their associated statistical problems. This allows the formulation of the cost‐effectiveness problem within a standard regression type framework. We provide an example with empirical data to illustrate how a regression type framework can enhance the net‐benefit method. We go on to suggest that practical advantages of the net‐benefit regression approach include being able to use established econometric techniques, adjust for imperfect randomisation, and identify important subgroups in order to estimate the marginal cost‐effectiveness of an intervention. Copyright © 2002 John Wiley & Sons, Ltd.

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  • 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.
  • Handle: RePEc:wly:hlthec:v:11:y:2002:i:5:p:415-430
    DOI: 10.1002/hec.678
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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. 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.
    3. Daniel Polsky & Henry A. Glick & Richard Willke & Kevin Schulman, 1997. "Confidence Intervals for Cost–Effectiveness Ratios: A Comparison of Four Methods," Health Economics, John Wiley & Sons, Ltd., vol. 6(3), pages 243-252, May.
    4. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    5. Andrew M. Jones, 2012. "health econometrics," The New Palgrave Dictionary of Economics,, Palgrave Macmillan.
    6. Andrew Briggs & Paul Fenn, 1998. "Confidence intervals or surfaces? Uncertainty on the cost‐effectiveness plane," Health Economics, John Wiley & Sons, Ltd., vol. 7(8), pages 723-740, December.
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