PD63 Impact Of Parallel Submission On The Rollout Time and Health-Technology-Assessment Recommendation Of New-Active-Substances
Bibliographic record
Abstract
Introduction Australia and Canada have parallel submission processes that allow companies to submit their dossier to the respective Health-Technology-Assessment (HTA) body before the market authorization is issued, aiming to provide timelier access to drugs. The objective of this study is to investigate the associations of parallel submissions with the rollout times and HTA recommendations of new active substances (NASs). Methods Public data from 208 HTA appraisals were collected from the Pharmaceutical Benefits Advisory Committee (PBAC) from Australia and the Canadian Agency for Drugs and Technology in Health (CADTH) for NASs obtaining regulatory approval between 2012 and 2020. We implemented multivariable logistic and linear regression models allowing for type of submission (parallel or sequential) and jurisdiction (Australia and Canada) to examine associations with first HTA recommendation (positive and positive with restrictions vs negative) and rollout time (regulatory submission to HTA recommendation), respectively. Results A total of 121 appraisals followed a parallel submission. The therapeutic products that most used a parallel submission were antineoplastic agents (Anatomical Therapeutic Chemical Code=L;47.11%). A similar proportion of chemical and biotechnological products followed parallel submissions. Multivariable linear regression showed that parallel submission presented 14-months decrease in rollout time when compared to sequential (p<0.001). Regarding jurisdictions, longer rollout times were seen for Canada when compared to Australia (β:4.0, p-value=0.024). Parallel submission showed no association with HTA recommendation. Canada had higher odds of receiving a positive recommendation (Odds Ratio:4.84, 95% confidence interval:2.63-9.18) when compared with Australia (p<0.001). Conclusions Antineoplastic agents were the main products using parallel submissions. Appraisals following a parallel submission showed a considerably faster rollout time than those following the traditional sequential submission, illustrating the advantage of this approach for dossier submission. The submission type did not have an impact on the HTA recommendation, indicating that although quicker, the HTA decision was not affected. Canada has a more restrictive criteria regarding the timing of dossier submission compared to Australia, which may lead to disparities in their rollout time.
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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 | 0.021 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".