IDEAS home Printed from https://ideas.repec.org/a/wly/hlthec/v22y2013i4p486-500.html
   My bibliography  Save this article

Statistical Methods For Cost‐Effectiveness Analyses That Use Observational Data: A Critical Appraisal Tool And Review Of Current Practice

Author

Listed:
  • Noémi Kreif
  • Richard Grieve
  • M. Zia Sadique

Abstract

Many cost‐effectiveness analyses (CEAs) use data from observational studies. Statistical methods can only address selection bias if they make plausible assumptions. No quality assessment tool is available for appraising CEAs that use observational studies. We developed a new checklist to assess statistical methods for addressing selection bias in CEAs that use observational data. The checklist criteria were informed by a conceptual review and applied in a systematic review of economic evaluations. Criteria included whether the study assessed the ‘no unobserved confounding’ assumption, overlap of baseline covariates between the treatment groups and the specification of the regression models. The checklist also considered structural uncertainty from the choice of statistical approach. We found 81 studies that met the inclusion criteria: studies tended to use regression (51%), matching on individual covariates (25%) or matching on the propensity score (22%). Most studies (77%) did not assess the ‘no observed confounding’ assumption, and few studies (16%) fully considered structural uncertainty from the choice of statistical approach. We conclude that published CEAs do not assess the main assumptions behind statistical methods for addressing selection bias. This checklist can raise awareness about the assumptions behind statistical methods for addressing selection bias and can complement existing method guidelines for CEAs. Copyright © 2012 John Wiley & Sons, Ltd.

Suggested Citation

  • Noémi Kreif & Richard Grieve & M. Zia Sadique, 2013. "Statistical Methods For Cost‐Effectiveness Analyses That Use Observational Data: A Critical Appraisal Tool And Review Of Current Practice," Health Economics, John Wiley & Sons, Ltd., vol. 22(4), pages 486-500, April.
  • Handle: RePEc:wly:hlthec:v:22:y:2013:i:4:p:486-500
    DOI: 10.1002/hec.2806
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hec.2806
    Download Restriction: no

    References listed on IDEAS

    as
    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    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. 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.
    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. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    6. Nuala A Sheehan & Vanessa Didelez & Paul R Burton & Martin D Tobin, 2008. "Mendelian Randomisation and Causal Inference in Observational Epidemiology," PLOS Medicine, Public Library of Science, vol. 5(8), pages 1-6, August.
    7. Anirban Basu & Willard G. Manning & John Mullahy, 2004. "Comparing alternative models: log vs Cox proportional hazard?," Health Economics, John Wiley & Sons, Ltd., vol. 13(8), pages 749-765, August.
    8. Joffe, M. & Mindell, J., 2006. "Complex causal process diagrams for analyzing the health impacts of policy interventions," American Journal of Public Health, American Public Health Association, vol. 96(3), pages 473-479.
    9. Thompson, Simon G. & Nixon, Richard M. & Grieve, Richard, 2006. "Addressing the issues that arise in analysing multicentre cost data, with application to a multinational study," Journal of Health Economics, Elsevier, vol. 25(6), pages 1015-1028, November.
    10. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157.
    11. Jones, A.M, 2010. "Models For Health Care," Health, Econometrics and Data Group (HEDG) Working Papers 10/01, HEDG, c/o Department of Economics, University of York.
    12. Harold C Sox & Mark Helfand & Jeremy Grimshaw & Kay Dickersin & the PLoS Medicine Editors & David Tovey & J André Knottnerus & Peter Tugwell, 2010. "Comparative Effectiveness Research: Challenges for Medical Journals," PLOS Medicine, Public Library of Science, vol. 7(4), pages 1-2, April.
    13. Steven C. Hill & G. Edward Miller, 2010. "Health expenditure estimation and functional form: applications of the generalized gamma and extended estimating equations models," Health Economics, John Wiley & Sons, Ltd., vol. 19(5), pages 608-627, May.
    14. John Mullahy, 2011. "Symposium on genetic data in health economics research," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 883-883, August.
    15. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    16. Glick, Henry A & Doshi, Jalpa A & Sonnad, Seema S & Polsky, Daniel, 2007. "Economic Evaluation in Clinical Trials," OUP Catalogue, Oxford University Press, number 9780198529972.
    17. Andrew M. Jones, 2007. "Identification of treatment effects in Health Economics," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1127-1131.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Deidda, Manuela & Geue, Claudia & Kreif, Noemi & Dundas, Ruth & McIntosh, Emma, 2019. "A framework for conducting economic evaluations alongside natural experiments," Social Science & Medicine, Elsevier, vol. 220(C), pages 353-361.
    2. Elsa Bouée-Benhamiche & Philippe Jean Bousquet & Salah Ghabri, 2020. "Economic Evaluations of Anticancer Drugs Based on Medico-Administrative Databases: A Systematic Literature Review," Applied Health Economics and Health Policy, Springer, vol. 18(4), pages 491-508, August.
    3. Chris Schilling & Dennis Petrie & Michelle M. Dowsey & Peter F. Choong & Philip Clarke, 2017. "The Impact of Regression to the Mean on Economic Evaluation in Quasi‐Experimental Pre–Post Studies: The Example of Total Knee Replacement Using Data from the Osteoarthritis Initiative," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 35-51, December.

    More about this item

    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:wly:hlthec:v:22:y:2013:i:4:p:486-500. 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: (Wiley Content Delivery). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/5749 .

    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.