How do HTA agencies perceive conditional approval of medicines? Evidence from England, Scotland, France and Canada
Bibliographic record
Abstract
There is a growing disconnect between regulatory agencies that are promoting expedited approval to medicines based on early phase clinical evidence and health technology assessment (HTA) agencies that require robust clinical evidence to inform coverage decisions. This paper provides an assessment of the evidence gap between regulatory and HTA agencies on medicines receiving conditional marketing authorisation (CMA) and examines how HTA agencies in France, England, Scotland, and Canada interpret and appraise evidence for these medicines. A mixed methods research design was used to identify the types and frequency of parameters raised in the context of HTA decision-making for all conditional approvals in Europe and Canada between 2010 and 2017. Significant heterogeneity was found across the HTA agencies in England, Scotland, France, and Canada in the assessment of medicines receiving CMA, with the highest likelihood of rejection present in Quebec (50%) and Scotland (25%). Rejected medicines were more likely to have unresolved uncertainties related to the magnitude of clinical benefit, study design, and issues in economic modelling. More systematic use of joint early dialogue and conditional reimbursement pathways would help clarify evidence requirements and avoid delays in patient access to innovative medicines.
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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 | 0.043 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".