Health technology assessment of drugs for rare diseases: insights, trends, and reasons for negative recommendations from the CADTH common drug review
Bibliographic record
Abstract
BACKGROUND: A shift in biochemical research towards drugs for rare diseases has created new challenges for the pharmaceutical industry, government regulators, health technology assessment agencies, and public and private payers. In this article, we aim to comprehensively review, characterize, identify possible trends, and explore reasons for negative reimbursement recommendations in submissions made to the Common Drug Review (CDR) for drugs for rare diseases (DRD) at the Canadian Agency for Drugs and Technologies in Health (CADTH), a publicly funded pan-Canadian health technology assessment agency. A public database (cadth.ca) was screened to identify DRD submissions to CDR. A diseases prevalence of ≤50 per 100,000 people was considered a rare disease. We calculated descriptive statistics for prevalence, study design, study size, treatment cost, reimbursement recommendation types, and reasons for negative reimbursement recommendations. RESULTS: From 2004 to 2015, 63 of 434 submissions to the CDR were for DRD (range: 1 submission in 2005 to 10 submissions in 2013). Most (74.6%) submissions included at least one double-blind randomized controlled trial (RCT). The average study size was 190 patients (range: 20 to 742). The average annual treatment cost was C$215,631 (range: $9,706 to $940,084). Reimbursement recommendations were positive for 54% of the submissions. Negative reimbursement recommendations were made due to a lack of clinical effectiveness (38.5%), insufficient evidence (30.8%), multiple reasons (23.1%), or lack of cost effectiveness/high cost (7.7%). CONCLUSION: The number of DRD submissions to CDR increased since 2013; from 4 to 5 per year between 2004 and 2012, to 10, 9, and 8 in 2013, 2014, and 2015 respectively. More than half of DRD submissions received positive reimbursement recommendation. Poor quality evidence and/or lack of supportive clinical evidence was at least partly responsible for a negative reimbursement recommendation in all cases. Although the average cost of DRD treatments was high, high cost was a reason for a negative reimbursement recommendation in only two (7.7%) of negative reimbursement recommendations.
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How this classification was reachedexpand
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".