IDEAS home Printed from https://ideas.repec.org/p/yor/hectdg/05-13.html
   My bibliography  Save this paper

Generalisable regression methods for costeffectiveness using copulas

Author

Listed:
  • Casey Quinn

Abstract

Objectives: Covariate explanation of clinical trial cost and outcome is critical to allow reliable estimates of cost-effectiveness. The ordinary simultaneous equations approach however must specify a bivariate distribution for both cost and health outcome that is not typically a product of the best-fitting marginal distribution of each. This paper advocates estimating costs and outcomes simultaneously using copulas to model conditional dependence. The copula is a function that maps univariate marginal distributions of any to some joint distribution. Methods- Copulas are used to fit the bivariate distribution of the simultaneous model for individual cost and outcome in a clinical trial for hysterectomy. These are used to generate counter-factual outcomes and individual-level incremental net benefits due to each procedure, as well as replicating non-parametric techniques for comparison with standard methods. Results- Parametric results from the use of copulas compared to an ordinary Seemingly Unrelated Regression model show better fit with consistent estimates, allowing for the fact that the data is drawn from an underpowered clinical trial. Results also show that estimated coefficients vary in size, sign and statistical significance in different arms of the clinical trial. Non-parametric results compared with standard cost-effectiveness techniques also show more precise estimates of incremental net benefit. Conclusions- Regression-based approaches to cost-effectiveness have the potential to overcome a lot of the limiting assumptions made using non-parametric approaches. By using known information on covariates we can get more precise estimates of the parameters used in standard cost-effectiveness analysis, more precise posterior information and more precise posterior probabilities. Using copulas generates more precise estimation of conditionally-dependent marginal effects.

Suggested Citation

  • Casey Quinn, 2005. "Generalisable regression methods for costeffectiveness using copulas," Health, Econometrics and Data Group (HEDG) Working Papers 05/13, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:05/13
    as

    Download full text from publisher

    File URL: https://www.york.ac.uk/media/economics/documents/herc/wp/05_13.pdf
    File Function: Main text
    Download Restriction: no

    References listed on IDEAS

    as
    1. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, 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. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost-effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787.
    4. Murray D. Smith, 2003. "Modelling sample selection using Archimedean copulas," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 99-123, June.
    5. Elisabetta Strazzera & Margarita Genius, 2004. "The Copula Approach to Sample Selection Modelling: An Application to the Recreational Value of Forests," Working Papers 2004.73, Fondazione Eni Enrico Mattei.
    6. 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.
    7. Jun Shao, 1990. "Bootstrap estimation of the asymptotic variances of statistical functionals," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 42(4), pages 737-752, December.
    8. Valentino Dardanoni & Peter Lambert, 2001. "Horizontal inequity comparisons," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 18(4), pages 799-816.
    9. 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.
    10. Jean-David Fermanian, 2003. "Goodness of Fit Tests for Copulas," Working Papers 2003-34, Center for Research in Economics and Statistics.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Cost-effectiveness analysis; incremental net benefit; simultaneous equations; copula;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I10 - Health, Education, and Welfare - - Health - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:yor:hectdg:05/13. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jane Rawlings). General contact details of provider: http://edirc.repec.org/data/deyoruk.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.