Good Practice Guidelines for the use of Statistical Regression Models in Economic Evaluations
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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.
KeywordsEconomic Evaluation Technology Appraisal Statistical Regression Model Single Technology Appraisal Final Statistical Model
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.
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