Methodological challenges in pharmacoeconomic submissions for cancer drug reimbursement in Canada from 2019-2021
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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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 | 0.270 | 0.618 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.016 | 0.027 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".