Acceptance of health technology assessment submissions with incremental cost-effectiveness ratios above the cost-effectiveness threshold
Bibliographic record
Abstract
Elizabeth A Griffiths, Janek K Hendrich, Samuel DR Stoddart, Sean CM Walsh HERON™ Commercialization, PAREXEL International, London, UK Objectives: In health technology assessment (HTA) agencies where cost-effectiveness plays a role in decision-making, an incremental cost-effectiveness ratio (ICER) threshold is often used to inform reimbursement decisions. The acceptance of submissions with ICERs higher than the threshold was assessed across different agencies and across indications, in order to inform future reimbursement submissions. Methods: All HTA appraisals from May 2000 to May 2014 from National Institute for Health and Care Excellence (NICE), Scottish Medicines Consortium (SMC), Pharmaceutical Benefits Advisory Committee (PBAC), and Canadian Agency for Drugs and Technologies in Health (CADTH) were assessed. Multiple technology appraisals, resubmissions, vaccination programs, and requests for advice were excluded. Submissions not reporting an ICER, or for which an ICER could not be determined were also excluded. The remaining appraisals were reviewed, and the submitted ICER, recommendation, and reasoning behind the recommendation were extracted. Results: NICE recommended the highest proportion of submissions with ICERs higher than the threshold (34% accepted without restrictions; 20% with restrictions), followed by PBAC (16% accepted without restrictions; 4% with restrictions), SMC (11% accepted without restrictions; 14% accepted with restrictions), and CADTH (0% accepted without restrictions; 26% with restrictions). Overall, the majority of higher-than-threshold ICER submissions were classified into the "malignant disease and immunosuppression" therapeutic category; however, there was no notable variation in acceptance rates by disease area. Reasons for accepting submissions reporting ICERs above the threshold included high clinical benefit over the standard of care, and addressing an unmet therapeutic need. Conclusion: Acceptance of submissions with higher-than-threshold ICERs varied by HTA agency and was not significantly influenced by disease category. Such submissions must be accompanied by robust, concrete, and transparent evidence in order to achieve patient access. Keywords: decision-making, incremental cost-effectiveness ratio, health technology assessment, QALY, cost-effectiveness
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.424 | 0.806 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".