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.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".