PP33 The Cost-effectiveness Of Regorafenib In The Treatment Of Advanced Hepatocellular Carcinoma From A Canadian Perspective
Bibliographic record
Abstract
Introduction Hepatocellular carcinoma (HCC) is the most common form of liver cancer and the fourth leading cause of cancer-related death globally. There are unmet needs for effective systematic therapy. The findings of the RESORCE trial highlighted the improvement in overall survival with regorafenib in advanced HCC patients progressing on sorafenib treatment. This study aimed to assess the cost-effectiveness of regorafenib compared with best supportive care (BSC) for advanced HCC from the Canadian healthcare system perspective. Methods We developed a Markov model based on four health states: live with adverse events, live without adverse events, progression, and dead. Health outcomes were measured using life-years (LYs), and quality-adjusted life-years (QALYs), and costs were presented in Canadian dollars (CAD). Clinical inputs were derived from the RESORCE trial. A 1.5 percent discount rate was applied to costs and outcomes. One-way and probabilistic sensitivity analyses were performed to assess the uncertainty in findings due to variability in parameters. TreeAge Pro software was used for model implementation. Results The use of regorafenib results in a gain of 0.38 LYs and 0.25 QALYs as compared to BSC with a high incremental cost of CAD26,954 (USD22,313). The ICER for regorafenib compared with BSC was CAD105,850/QALY (USD87,624/QALY) in the base-case analysis. Further, probabilistic sensitivity analyses revealed regorafenib not to be cost-effective at a willingness-to-pay threshold of CAD50,000/QALY. Conclusions Regorafenib was not found to be cost-effective in the treatment of advanced HCC because of the lower health benefits and higher incremental costs. Lowering the official price of regorafenib or use for only selected patients who can achieve maximum benefits would enhance its cost-effectiveness and treatment preference value.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".