Examining the association between oncology drug clinical benefit and the time to public reimbursement
Bibliographic record
Abstract
BACKGROUND: We examined if oncology drug indications with high clinical benefit, as measured by the American Society of Clinical Oncology Value Framework (ASCO-VF) and European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS), received public reimbursement status faster than those with lower clinical benefit from the time of pan-Canadian Oncology Drug Review (pCODR) recommendation. METHODS: Oncology drug indications submitted to pCODR between July 2011 and October 2018 were examined. Included indications had a regulatory approval date, completed the pCODR review process, received a positive pCODR recommendation, and been funded by at least one province. Trials cited for clinical efficacy were used to determine the clinical benefit (per ASCO-VF and ESMO-MCBS) of drug indications. RESULTS: Eighty-four indications were identified, yielding 65 ASCO-VF and 50 ESMO-MCBS scores. The mean ASCO-VF and ESMO-MCBS scores were 44.9 (SD = 21.1) and 3.3 (SD = 1.0), respectively. The mean time to provincial reimbursement from pCODR recommendation was 13.2 months (SD = 9.3 months). Higher ASCO-VF and ESMO-MCBS scores had low correlation with shorter time to reimbursement, (ρ = -0.21) and (ρ = 0.24), respectively. In the multivariable analyses, ASCO-VF (p = 0.40) and ESMO-MCBS (p = 0.31) scores were not significantly associated with time to reimbursement. Province and year of pCODR recommendation were associated with time to reimbursement in both ASCO and ESMO models. CONCLUSIONS: Oncology drug indications with higher clinical benefit do not appear to be reimbursed faster than those with low clinical benefit. This suggests the need to prioritize oncology drug indications based on clinical benefit to ensure quicker access to oncology drugs with the greatest benefits.
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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 | 0.014 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".