Examining the relationship between the clinical benefit of oncology drug indications and the time from pan-Canadian Oncology Drug Review (pCODR) recommendation to public reimbursement.
Bibliographic record
Abstract
e19360 Background: We examined if publicly reimbursed oncology drug indications with evidence of high clinical benefit, as measured by the American Society of Clinical Oncology Value Framework v2 (ASCO-VF), and European Society for Medical Oncology Magnitude of Clinical Benefit Scale v1.1 (ESMO-MCBS), received reimbursement status faster than those with lower clinical benefit from the time of pCODR recommendation. Methods: Oncology drug indications that received pCODR recommendations between Jan 2012 and July 2018 were identified. Indications that did not receive provincial reimbursement, without notice of compliance, or received a negative pCODR recommendation were excluded. The relationship between clinical benefit, as measured by ASCO-VF and ESMO-MCBS, and the time to reimbursement was evaluated using Spearman correlation coefficient, univariable, and multivariable linear regression analyses. Results: Overall, 84 indications met inclusion criteria yielding 80 ASCO-VF and 66 ESMO-MCBS scores. The mean ASCO-VF and ESMO-MCBS scores were 38.8 (SD = 23.8) and 3.0 (SD = 1.1) respectively. Higher ASCO-VF and ESMO-MCBS scores had low correlation with shorter time to provincial funding, (rho = -0.15, 95%CI -0.24, -0.06) and (rho = -0.25, 95%CI -0.34, -0.16) respectively. Univariable analyses showed that manufacturer reported incremental cost effectiveness ratio (ICER) values, year of pCODR recommendation, province and cancer type were associated with time to public reimbursement (all p < 0.0001). After adjusting for potential confounders in the respective multivariable analysis, ASCO-VF (p = 0.29) and ESMO-MCBS (p = 0.15) scores were not significantly associated with time to public reimbursement. Year of pCODR recommendation remained associated with time to public reimbursement (p < 0.001). Earlier years (2012-2014) had a shorter time to reimbursement (mean = 10.4 months) than later years (2015-2018) (mean = 14.5 months). Other factors that were associated with time to reimbursement in multivariable analysis were province (p < 0.001) and cancer type (p < 0.001). Conclusions: Currently, oncology drug indication with evidence of high clinical benefit do not appear to be funded faster than those with low clinical benefit. This suggests the need to prioritize cancer drug indications based on clinical benefit in order to allow for timely public reimbursement of cancer drugs with higher clinical benefit to patients.
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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 | 0.017 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".