A rational framework for decision making by the National Institute for Clinical Excellence (NICE)
Abstract
Regulatory and reimbursement authorities face uncertain choices when considering the adoption of health-care technologies. In this Viewpoint, we present an analytic framework that separates the issue of whether a technology should be adopted on the basis of existing evidence from whether more research should be demanded to support future decisions. We show the application of this framework to the assessment of heath-care technologies using a published analysis of a new drug treatment for Alzheimer's disease. The results of the analysis show that the amount and type of evidence required to support the adoption of a health technology will differ substantially between technologies with different characteristics. Additionally, the analysis can be used to aid the efficient design of research. We discuss the implications of adoption of this new framework for regulatory and reimbursement decisions.
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The expected value of perfect information (EVPI) is perhaps the best measure of uncertainty surrounding a particular decision in cost-effectiveness analysis, since it combines the probability of incorrect decision making with the consequential loss function.
“Perhaps the best measure of uncertainty surrounding a particular decision in cost-effectiveness analysis is the EVPI, since this measure combines the probability of incorrect decision making with the consequential loss function [28].”
From Model Parameter Estimation and Uncertainty: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6 (2012) · cites this paper as [28]
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References 19 · Cited by 456
- Authors
- Claxton, K., Sculpher, M., & Drummond, M.
- Affiliations
- University of York (GB)
- Corresponding author
- Claxton, Karl
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2002-08
- Volume
- 360
- Issue
- 9334
- Pages
- 711-715
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- OpenAlex
- W2159390189
- Cited by
- 456
- References
- 19
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 6 · 2023: 6 · 2022: 10 · 2021: 7 · 2020: 7 · 2019: 21 · 2018: 16 · 2017: 26 · 2016: 19 · 2015: 21 · 2014: 18 · 2013: 24 · 2012: 19
- FWCI
- 38.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet (publishedVersion)
- PubMed (publishedVersion)
- DigitalGeorgetown (Georgetown University Library) (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Pharmaceutical Economics and Policy
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- health technology assessment, Nice, decision-making, analytical framework, research needs, regulatory decision-making, reimbursement decisions, Alzheimer's disease, pharmacotherapy, research design
- MeSH terms
- Alzheimer Disease, Decision Support Techniques, Diffusion of Innovation, Drug and Narcotic Control, United Kingdom, Humans, Reimbursement Mechanisms, Research Design, State Medicine, Drugs, Investigational, Randomized Controlled Trials as Topic, Drug Approval
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