Health Technology Assessment Reports for Non-Oncology Medications in Canada from 2018 to 2022: Methodological Critiques on Manufacturers’ Submissions and a Comparison between Manufacturer and Canadian Agency for Drugs and Technologies in Health (CADTH) Analyses
Bibliographic record
Abstract
INTRODUCTION: Identifying key differences between manufacturers' submitted analysis and economic reanalysis by the Canadian Agency for Drugs and Technologies in Health (CADTH) is an important step toward understanding reimbursement recommendations. We compared economic values reported in manufacturers' analysis with the CADTH reanalysis and also assessed methodological critiques. METHODS: Two reviewers extracted data from the clinical and economic reports in publicly available CADTH reports from 2018 to 2022. We used the Wilcoxon rank-sum test to assess the difference between mean economic values, and the Chi-square test to assess the association between the CADTH critique final recommendations. RESULTS: Of the total submissions, 99.4% included effectiveness critiques, 88.8% included model structure critiques, 69.1% included utility score critiques, and 78.7% included cost critiques. The median incremental cost-utility ratio (ICUR) in the manufacturers' analyses was $138,658/quality-adjusted life-year (QALY), 2.5-fold lower than the CADTH's reanalysis at $380,251/QALY (p < 0.001). The median CADTH reanalysis for 3-year budget impact analysis (BIA) was $4,575,102, which was 27% higher than the manufacturers' submitted 3-year BIA (p < 0.001). CADTH requested a price reduction for 95% of all submissions, and the median price reduction request was 63.5%. In 2021 and 2022, the willingness-to-pay threshold identified in CADTH reports remained constant at $50,000 per QALY gained for all medications. CONCLUSION: There was high frequency of CADTH critiques on manufacturers' submissions in all four aspects of economic submissions: effectiveness, cost, utility score and structure. We observed a higher median incremental cost and lower median incremental QALYs in the CADTH reanalysis compared with the manufacturers' submissions. The resulting higher ICUR in the CADTH reanalysis often leads to a recommendation that the manufacturer needs to reduce its price. The 3-year budget impact was higher in the CADTH reanalyses compared with manufacturers' submissions.
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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 | 0.577 | 0.878 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.031 | 0.040 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| 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".