Reimbursement recommendations for cancer drugs supported by phase II evidence in Canada.
Bibliographic record
Abstract
e14133 Background: Historically, pharmaceutical companies submitted phase III evidence for consideration of public reimbursement; however, phase II data is being more commonly used as primary evidence. Whether submissions with phase II data lead to similar rates of positive reimbursement recommendations as phase III data has not been comprehensively investigated. We compared frequency of reimbursement recommendations between phase II and phase III submissions for oncologic drugs and assessed for factors associated with a positive or conditional recommendation. Methods: We identified all submissions with phase II data from the CADTH pCODR’s expert review committee (pERC) recommendations from July 2011 to July 2019. We identified fourteen binary variables relating to clinical benefit, patient-based values, economic impact, and adoption feasibility. We used Fisher’s exact test to characterize associations between all variables and the final recommendation. We conducted multivariable analysis with logistic regression for three variables: feasibility of phase III study, hematologic indication, and unmet need. Results: We identified 139 submissions with a pERC final recommendation. Twenty-seven (19%) submissions were supported by phase II evidence, with 63% having a positive recommendation in comparison to 82% among submissions with phase III evidence. Clinical benefit (p < 0.001), gap in current treatment standards (p = 0.047), and patient alignment (p = 0.015) were associated with a positive recommendation, whereas the future feasibility of conducting a phase III study was associated with a negative recommendation (p = 0.040). No significant association was found between the recommendation and factors related to cost effectiveness or adoption feasibility. In multivariable analysis, only feasibility of a phase III study was significantly associated with a negative recommendation (p = 0.024, OR = 0.132). Conclusions: Oncologic submissions with phase II data were less likely to be recommended for public reimbursement than phase III studies. Positive or conditional recommendation was more likely if they demonstrated clinical benefit and aligned with patient values. pERC was less likely to recommend a submission with phase II if a phase III trial was either possible or already initiated.
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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 | 0.347 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".