Methodological challenges in pharmacoeconomic submissions for cancer drug reimbursement in Canada from 2019-2021
Why this work is in the frame
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Bibliographic record
Abstract
Cancer is the leading cause of death in Canada, carrying a mortality rate of 1 in 4 Canadians. As new therapeutic options are developed, high drug costs place a huge burden on patients and the healthcare system. Due to budget limitations, not all pharmaceuticals can be publicly funded, so difficult decisions must be made for which drugs are funded and in what contexts. The pan-Canadian Oncology Drug Review (pCODR) evaluates submissions for new oncological pharmaceuticals to make reimbursement recommendations. Comprehensive analyses assessing limitations in recent oncology drug submissions have not yet been performed. The objective of this project, therefore, was to characterize common limitations in recent oncological drug submissions and to determine the impacts of their reanalyses on the incremental cost-effectiveness ratio (ICER). Oncological pharmaceutical appraisals for which pCODR generated a final recommendation in 2019, 2020, and 2021 were included (n= 64). An extraction form was created with 18 categories for grouping of limitations. Limitation frequencies and effects of pCODR’s reanalyses on ICER (in $/Quality Adjusted Life Year (QALY)) were extracted. The most commonly identified limitations pertained to extrapolation and costs. Reanalyses for limitations pertaining to natural history characterization/model structure, comparators, and duration of treatment effect resulted in the greatest median impacts on ICER. Recognizing common limitations and assessing their impacts on ICER can assist in improving the quality of future drug submissions. It can help to ensure that CADTH receives robust submissions for which they can efficiently and accurately make reimbursement recommendations. Furthermore, such characterizations can inform potential adjustments to be made to existing guidelines to assist drug manufacturers in avoiding common limitations in their submitted models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it