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Record W3142649038

The NICE Cost-Effectiveness Threshold: What it is and What that Means

2008· article· en· W3142649038 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMPRA Paper · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNiceExcellenceReimbursementEquity (law)Actuarial scienceCost effectivenessHealth technologyPsychological interventionEconomicsMedicineBusinessHealth carePublic economicsRisk analysis (engineering)Computer sciencePolitical scienceEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

The National Institute for Health and Clinical Excellence (NICE) has been using a cost-effectiveness threshold range between £20 000 and £30 000 for over 7 years. What the cost-effectiveness threshold represents, what the appropriate level is for NICE to use, and what the other factors are that NICE should consider have all been the subject of much discussion. In this article, we briefly review hese questions, provide a critical assessment of NICE’s utilization of the incremental cost-effectiveness ratio (ICER) threshold to inform its guidance, and suggest ways in which NICE’s utilization of the ICER threshold could be developed to promote the efficient use of health service resources. We conclude that it is feasible and probably desirable to operate an explicit single threshold rather than the current range; the threshold should be seen as a threshold at which ‘other’ criteria beyond the ICER itself are taken into account; interventions with a large budgetary impact may need to be subject to a lower threshold as they are likely to displace more than the marginal activities; reimbursement at the threshold transfers the full value of an innovation to the manufacturer. Positive decisions above the threshold on the grounds of innovation reduce population health; the value of the threshold should be reconsidered regularly to ensure that it captures the impact of changes in efficiency and budget over time; the use of equity weights to sustain a positive recommendation when the ICER is above the threshold requires knowledge of the equity characteristics of those patients who bear the opportunity cost. Given the barriers to obtaining this knowledge and knowledge about the characteristics of typical beneficiaries of UK NHS care, caution is warranted before accepting claims from special pleaders; uncertainty in the evidence base should not be used to justify a positive recommendation when the ICER is above the threshold. The development of a programme of disinvestment guidance would enable NICE and the NHS to be more confident that the net health benefit of the Technology Appraisal Programme is positive.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.401
GPT teacher head0.419
Teacher spread0.018 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it