The value-for-money assessment and funding arrangements for high-priced drugs in an era of uncertainty: a comparative analysis of national health technology assessment agencies in South Korea, England, Australia, and Canada
Bibliographic record
Abstract
BACKGROUND: Innovative health technologies have increasingly emerged as a promising solution for patients with untreatable or challenging conditions. However, these technologies often come with expensive costs and limited evidence at the time of launch. This study assessed how these high-priced drugs with limited evidence were appraised and introduced in South Korea, England, Australia, and Canada, where cost-effectiveness analysis (CEA) generally plays a central role in pricing and reimbursement decisions. METHODS: The study analysed 22 high-priced drugs (24 indications) introduced in South Korea, which were granted CEA waivers due to difficulties in evidence generation and high unmet needs. Data, including funding arrangements and evidence assessed, were derived from national health technology assessment (HTA) documents and other public domains in the four countries. RESULTS: Nearly all drugs received positive recommendations, largely through managed entry agreements (MEAs), particularly in England. Single-arm trials were more common in South Korea and England. Indirect comparison was the primary source of comparative effectiveness in England (70.0%), emphasising alignment with current practices. Australia and Canada utilised both indirect comparison and head-to-head trial data in similar proportions. Except for South Korea, all countries still required CEA data for these drugs. Data collection for coverage with evidence development was necessary in 55.0% of cases in England, and less in other countries. CONCLUSION: HTA agencies increasingly accept the uncertainty of high-priced drugs with high unmet needs through MEAs. To ensure timely access and value for money, implementing full value assessment and uncertainty management, while strengthening national and international collaboration for effective data collection, is imperative.
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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 | 0.040 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".