Association between oncology drug review times and public funding recommendations.
Bibliographic record
Abstract
99 Background: New oncology drugs undergo detailed review of clinical, economic, and patient data. Thoroughly assessing these data can require lengthy review processes, in the absence of accelerated approval pathways. The aim of this study was to assess how cancer drug review times impact public funding recommendations. Methods: Drugs reviewed by Canada’s health technology assessment body, the pan-Canadian Oncology Drug Review (pCODR), from April 2012 to November 2020 were included in this study. Data was collected including Health Canada approval date, initial and final funding recommendations, treatment intent, drug class, clinical indication (tumour type) and incremental cost-effectiveness ratios (ICER). Univariable and multivariable analyses were used to determine the association between funding recommendations and review times. Results: Of the 227 applications submitted to pCODR, 168 had received positive funding recommendations. Amongst the total drug applications, 24 (14.3%) drugs were intended for the treatment of thoracic cancers, 19 (11.3%) for gastrointestinal cancers, 17 (10.1%) for genitourinary cancers, 17 (10.1%) for breast cancer, and 91 (54.2%) for other tumour sites. Median time from pCODR submission to final recommendation was longer for drugs indicated for the treatment of lung and breast cancer compared to those indicated for treatment of other tumours (223 vs. 212 vs. 203 days, respectively; Kruskal-Wallis p = 0.0322). Drugs with longer review times were more likely to receive a negative pCODR recommendation, even when adjusting for ICER (157 vs 298 days, Wilcoxon p-value = 0.0003). There was no association between positive or negative funding recommendation and tumour type. Conclusions: Oncology drugs with longer review times are less likely to receive recommendation for public funding in Canada. Addressing factors contributing to variance in review times and standardizing the review process can ensure equitable access to cancer drugs.
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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 | 0.034 | 0.316 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".