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Regression methods for covariate adjustment and subgroup analysis for non‐censored cost‐effectiveness data

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

Abstract

The current interest in undertaking cost‐effectiveness analyses alongside clinical trials has lead to the increasing availability of patient‐level data on both the costs and effectiveness of intervention. In a recent paper, we show how cost‐effectiveness analysis can be undertaken in a regression framework. In the current paper we develop a direct regression approach to cost‐effectiveness analysis by proposing the use of a system of seemingly unrelated regression equations to provide a more general method for prognostic factor adjustment with emphasis on sub‐group analysis. This more general method can be used in either an incremental cost‐effectiveness or an incremental net‐benefit approach, and does not require that the set of independent variables for costs and effectiveness be the same. Furthermore, the method can exhibit efficiency gains over unrelated ordinary least squares regression. Copyright © 2003 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:hlthec:v:13:y:2004:i:5:p:461-475
    DOI: 10.1002/hec.843
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Daniel F. Heitjan, 2000. "Fieller's method and net health benefits," Health Economics, John Wiley & Sons, Ltd., vol. 9(4), pages 327-335, June.
    4. 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.
    5. 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.
    6. Andre Ament & Rob Baltussen, 1997. "The Interpretation of results of economic evaluation: explicating the value of health," Health Economics, John Wiley & Sons, Ltd., vol. 6(6), pages 625-635, November.
    7. Andrew H. Briggs & David E. Wonderling & Christopher Z. Mooney, 1997. "Pulling cost‐effectiveness analysis up by its bootstraps: A non‐parametric approach to confidence interval estimation," Health Economics, John Wiley & Sons, Ltd., vol. 6(4), pages 327-340, July.
    8. Andrew H. Briggs, 1999. "A Bayesian approach to stochastic cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 257-261, May.
    9. Manning, Willard G. & Mullahy, John, 2001. "Estimating log models: to transform or not to transform?," Journal of Health Economics, Elsevier, vol. 20(4), pages 461-494, July.
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