OP171 Does Parallel Regulatory-Health Technology Assessment Reviews Affect Time To Health Technology Assessment Decisions?
Bibliographic record
Abstract
Introduction: Timely recommendation by Health Technology Assessment (HTA) agencies for drug reimbursement is critical to ensure patient access to medicines of therapeutic value. In this study, HTA performance was examined in terms of their outcome and timing by looking at how 103 drugs, which gained regulatory approval from 2013 to 2015, were assessed by HTA agencies from 2014 to 2016. Methods: Products must have received regulatory approval from one of the following regulatory agencies: EMA (Europe), Health Canada (Canada) and TGA (Australia). The first HTA recommendations were then collected from PBAC (Australia), CADTH (Canada), HAS (France), IQWiG (Germany), SMC (Scotland) and TLV (Sweden). The HTA decisions were classified as positive, positive with restrictions, negative and multiple. Results: Eighty-four drugs were approved in Europe before Australia and Canada. Of the studied HTA agencies, PBAC had the highest percentage of products recommended within a year from regulatory approval (93 percent). In addition, Australia had the shortest median time between first regulatory submission by any of the three agencies and HTA recommendation (553 days) as compared to Europe (616 days) and Canada (722 days). This can be attributed to the TGA/PBAC parallel process. However, Australia has the highest proportion of products receiving a negative PBAC recommendation (62 percent). Conclusions: The majority of drugs were first submitted for reimbursement in Europe, but the time from regulatory submission to HTA decision was the fastest in Australia. This can be attributed to the TGA/PBAC parallel review process, which showed its benefit in reducing the overall time. A parallel review process is also available in Canada; however, it is not utilized as frequently by companies as in Australia.
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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 | 0.109 | 0.414 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
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".