, Volume 31, Issue 8, pp 643–652 | Cite as

Good Practice Guidelines for the use of Statistical Regression Models in Economic Evaluations

  • Ben Kearns
  • Roberta Ara
  • Allan Wailoo
  • Andrea Manca
  • Monica Hernández Alava
  • Keith Abrams
  • Mike Campbell
Practical Application


Decision-analytic models (DAMs) used to evaluate the cost effectiveness of interventions are pivotal sources of evidence used in economic evaluations. Parameter estimates used in the DAMs are often based on the results of a regression analysis, but there is little guidance relating to these. This study had two objectives. The first was to identify the frequency of use of regression models in economic evaluations, the parameters they inform, and the amount of information reported to describe and support the analyses. The second objective was to provide guidance to improve practice in this area, based on the review. The review concentrated on a random sample of economic evaluations submitted to the UK National Institute for Health and Clinical Excellence (NICE) as part of its technology appraisal process. Based on these findings, recommendations for good practice were drafted, together with a checklist for critiquing reporting standards in this area. Based on the results of this review, statistical regression models are in widespread use in DAMs used to support economic evaluations, yet reporting of basic information, such as the sample size used and measures of uncertainty, is limited. Recommendations were formed about how reporting standards could be improved to better meet the needs of decision makers. These recommendations are summarised in a checklist, which may be used by both those conducting regression analyses and those critiquing them, to identify what should be reported when using the results of a regression analysis within a DAM.


Economic Evaluation Technology Appraisal Statistical Regression Model Single Technology Appraisal Final Statistical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This article is based on a report that was funded by NICE through its Decision Support Unit. The views, and any errors or omissions, expressed in this article are of the authors only.

BK drafted the manuscript and takes responsibility as the overall guarantor of the content. BK, RA and AW conceived and planned the work. All authors contributed to drafting the checklist and revising the manuscript for important intellectual content. All authors have given their approval for the final version to be published.

The authors acknowledge Paul Tappenden, who contributed to the work, but did not meet the criteria for authorship.

AM is a member of the technology appraisal committee at NICE. BK, RA, AW, MHA, KA and MC have no other potential conflicts of interest. AM’s contribution was made under the terms of a Career Development research training fellowship issued by the National Institute for Health Research (NIHR; grant CDF-2009-02-21). The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, The NIHR or the Department of Health.


  1. 1.
    National Institute for Health and Clinical Excellence. Published technology appraisals. 16 Oct 2012.
  2. 2.
    Kearns B, Ara R, Waillo A. NICE Decision Support Unit: regression methods. 1-8-2013.
  3. 3.
    Cox DR, Snell EJ. Applied statistics: principles and examples. London: Chapman & Hall; 1981.Google Scholar
  4. 4.
    Chatfield C. The initial examination of data. J R Stat Soc A. 1985;148(3):214–53.CrossRefGoogle Scholar
  5. 5.
    National Institute for Health and Clinical Excellence. Guide to the methods of technology appraisal (updated June 2008). 2008.Google Scholar
  6. 6.
    Freeman JV, Walters SJ, Campbell MJ. How to display data. Oxford: BMJ Books; 2008.Google Scholar
  7. 7.
    Few S. Show me the numbers. California: Analytics Press; 2004.Google Scholar
  8. 8.
    Altman DG. Statistics and ethics in medical research. VI: presentation of results. Br Med J. 1980;281(6254):1542–4.CrossRefPubMedGoogle Scholar
  9. 9.
    Hand DJ. Statistics and data mining: intersecting disciplines. ACM SIGKDD Explor Newslett. 1999;1(1):16–9.CrossRefGoogle Scholar
  10. 10.
    Good P, Hardin J. Common mistakes in statistics (and how to avoid them). New Jersey: Wiley; 2003.CrossRefGoogle Scholar
  11. 11.
    Harrell FE, Lee KL, Mark DB. Tutorial in biostatistics multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15:361–87.CrossRefPubMedGoogle Scholar
  12. 12.
    Royston P, Suaerbrei W. Multivariable model-building: a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables. Chichester: Wiley; 2008.Google Scholar
  13. 13.
    Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. New York: Wiley; 2002.Google Scholar
  14. 14.
    Briggs A, Clark T, Wolstenholme J, Clarke P. Missing … presumed at random: cost-analysis of incomplete data. Health Econ. 2003;12(5):377–92.CrossRefPubMedGoogle Scholar
  15. 15.
    Briggs AH, Weinstein MC, Fenwick EA, Karnon J, Sculpher MJ, Paltiel AD. Model parameter estimation and uncertainty analysis a report of the ISPOR-SMDM modeling good research practices task force working group-6. Med Decision Mak. 2012;32(5):722–32.CrossRefGoogle Scholar
  16. 16.
    Campbell MJ. Statistics at square two: understanding modern statistical applications in medicine. Oxford: BMJ Books; 2006CrossRefGoogle Scholar
  17. 17.
    Cooper NJ, Sutton AJ, Mugford M, Abrams KR. Use of Bayesian Markov Chain Monte Carlo methods to model cost-of-illness data. Med Decision Mak. 2003;23(1):38–53.CrossRefGoogle Scholar
  18. 18.
    Tufte E. Improving data analysis in political science. World Politics. 1969;21(4):641–54.CrossRefGoogle Scholar
  19. 19.
    Machin D, Campbell MJ, Walters SJ. Medical statistics. 4th ed. Chichester: Wiley; 2007.Google Scholar
  20. 20.
    Ara R, Brazier J. Predicting the short form-6D preference-based index using the eight mean short form-36 health dimension scores: estimating preference-based health-related utilities when patient level data are not available. Value Health. 2009;12(2):346–53.CrossRefPubMedGoogle Scholar
  21. 21.
    Sullivan PW, Ghushchyan V. Preference-based EQ-5D index scores for chronic conditions in the United States. Med Decis Making. 2006;26(4):410–20.CrossRefPubMedGoogle Scholar
  22. 22.
    Sullivan PW, Slejko JF, Sculpher MJ, Ghushchyan V. Catalogue of EQ-5D scores for the United Kingdom. Med Decis Making. 2011;31(6):800–4.CrossRefPubMedGoogle Scholar
  23. 23.
    Dobrez D, Cella D, Pickard AS, Lai JS, Nickolov A. Estimation of patient preference-based utility weights from the functional assessment of cancer therapy-general. Value Health. 2007;10(4):266–72.CrossRefPubMedGoogle Scholar
  24. 24.
    Briggs A, Sculpher M, Claxton K. Decision modelling for health economic evaluation. Oxford: Oxford University Press; 2006.Google Scholar
  25. 25.
    Stevenson M, Tappenden P, Squires H. Methods for handling uncertainty within pharmaceutical funding decisions. Int J Syst Sci 2012. Available from:
  26. 26.
    Strong M, Oakley JE, Chilcott J. Managing structural uncertainty in health economic decision models: a discrepancy approach. J R Stat Soc Ser C. 2012;61(1):25–45.CrossRefGoogle Scholar
  27. 27.
    Husereau D, Drummond D, Petrou S, Carswell C, Moher D, Greenberg D, et al. Consolidated health economic evaluation reporting standards (CHEERS) statement. Pharmacoeconomics. 2013;31(5):361–7.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Ben Kearns
    • 1
  • Roberta Ara
    • 1
  • Allan Wailoo
    • 1
  • Andrea Manca
    • 2
  • Monica Hernández Alava
    • 1
  • Keith Abrams
    • 3
  • Mike Campbell
    • 1
  1. 1.School of Health and Related ResearchUniversity of SheffieldSheffieldUK
  2. 2.Centre for Health EconomicsUniversity of YorkYorkUK
  3. 3.School of MedicineUniversity of LeicesterLeicesterUK

Personalised recommendations