Skip to main content
Top
Published in: PharmacoEconomics 8/2013

01-08-2013 | Practical Application

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

Authors: Ben Kearns, Roberta Ara, Allan Wailoo, Andrea Manca, Monica Hernández Alava, Keith Abrams, Mike Campbell

Published in: PharmacoEconomics | Issue 8/2013

Login to get access

Abstract

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.
Appendix
Available only for authorised users
Literature
3.
go back to reference Cox DR, Snell EJ. Applied statistics: principles and examples. London: Chapman & Hall; 1981. Cox DR, Snell EJ. Applied statistics: principles and examples. London: Chapman & Hall; 1981.
4.
go back to reference Chatfield C. The initial examination of data. J R Stat Soc A. 1985;148(3):214–53.CrossRef Chatfield C. The initial examination of data. J R Stat Soc A. 1985;148(3):214–53.CrossRef
5.
go back to reference National Institute for Health and Clinical Excellence. Guide to the methods of technology appraisal (updated June 2008). 2008. National Institute for Health and Clinical Excellence. Guide to the methods of technology appraisal (updated June 2008). 2008.
6.
go back to reference Freeman JV, Walters SJ, Campbell MJ. How to display data. Oxford: BMJ Books; 2008. Freeman JV, Walters SJ, Campbell MJ. How to display data. Oxford: BMJ Books; 2008.
7.
go back to reference Few S. Show me the numbers. California: Analytics Press; 2004. Few S. Show me the numbers. California: Analytics Press; 2004.
8.
go back to reference Altman DG. Statistics and ethics in medical research. VI: presentation of results. Br Med J. 1980;281(6254):1542–4.CrossRefPubMed Altman DG. Statistics and ethics in medical research. VI: presentation of results. Br Med J. 1980;281(6254):1542–4.CrossRefPubMed
9.
go back to reference Hand DJ. Statistics and data mining: intersecting disciplines. ACM SIGKDD Explor Newslett. 1999;1(1):16–9.CrossRef Hand DJ. Statistics and data mining: intersecting disciplines. ACM SIGKDD Explor Newslett. 1999;1(1):16–9.CrossRef
10.
go back to reference Good P, Hardin J. Common mistakes in statistics (and how to avoid them). New Jersey: Wiley; 2003.CrossRef Good P, Hardin J. Common mistakes in statistics (and how to avoid them). New Jersey: Wiley; 2003.CrossRef
11.
go back to reference 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.CrossRefPubMed 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.CrossRefPubMed
12.
go back to reference Royston P, Suaerbrei W. Multivariable model-building: a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables. Chichester: Wiley; 2008. Royston P, Suaerbrei W. Multivariable model-building: a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables. Chichester: Wiley; 2008.
13.
go back to reference Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. New York: Wiley; 2002. Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. New York: Wiley; 2002.
14.
go back to reference Briggs A, Clark T, Wolstenholme J, Clarke P. Missing … presumed at random: cost-analysis of incomplete data. Health Econ. 2003;12(5):377–92.CrossRefPubMed Briggs A, Clark T, Wolstenholme J, Clarke P. Missing … presumed at random: cost-analysis of incomplete data. Health Econ. 2003;12(5):377–92.CrossRefPubMed
15.
go back to reference 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.CrossRef 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.CrossRef
16.
go back to reference Campbell MJ. Statistics at square two: understanding modern statistical applications in medicine. Oxford: BMJ Books; 2006CrossRef Campbell MJ. Statistics at square two: understanding modern statistical applications in medicine. Oxford: BMJ Books; 2006CrossRef
17.
go back to reference 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.CrossRef 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.CrossRef
18.
go back to reference Tufte E. Improving data analysis in political science. World Politics. 1969;21(4):641–54.CrossRef Tufte E. Improving data analysis in political science. World Politics. 1969;21(4):641–54.CrossRef
19.
go back to reference Machin D, Campbell MJ, Walters SJ. Medical statistics. 4th ed. Chichester: Wiley; 2007. Machin D, Campbell MJ, Walters SJ. Medical statistics. 4th ed. Chichester: Wiley; 2007.
20.
go back to reference 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.CrossRefPubMed 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.CrossRefPubMed
21.
go back to reference 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.CrossRefPubMed 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.CrossRefPubMed
22.
go back to reference 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.CrossRefPubMed 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.CrossRefPubMed
23.
go back to reference 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.CrossRefPubMed 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.CrossRefPubMed
24.
go back to reference Briggs A, Sculpher M, Claxton K. Decision modelling for health economic evaluation. Oxford: Oxford University Press; 2006. Briggs A, Sculpher M, Claxton K. Decision modelling for health economic evaluation. Oxford: Oxford University Press; 2006.
26.
go back to reference 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.CrossRef 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.CrossRef
27.
go back to reference 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.CrossRefPubMed 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.CrossRefPubMed
Metadata
Title
Good Practice Guidelines for the use of Statistical Regression Models in Economic Evaluations
Authors
Ben Kearns
Roberta Ara
Allan Wailoo
Andrea Manca
Monica Hernández Alava
Keith Abrams
Mike Campbell
Publication date
01-08-2013
Publisher
Springer International Publishing
Published in
PharmacoEconomics / Issue 8/2013
Print ISSN: 1170-7690
Electronic ISSN: 1179-2027
DOI
https://doi.org/10.1007/s40273-013-0069-y

Other articles of this Issue 8/2013

PharmacoEconomics 8/2013 Go to the issue

Original Research Article

A Choice That Matters?