The Cost‐Effectiveness of Lenvatinib in the Treatment of Advanced or Unresectable Hepatocellular Carcinoma from a Canadian Perspective
Bibliographic record
Abstract
Lenvatinib is an oral multikinase inhibitor indicated for the first-line treatment of unresectable hepatocellular carcinoma (uHCC). In the Phase III REFLECT trial, lenvatinib was noninferior in the primary endpoint of overall survival versus sorafenib, the only systemic therapy funded in Canada prior to the introduction of lenvatinib. Lenvatinib also demonstrated statistically significant improvement compared to sorafenib in secondary endpoint progression-free survival, time to progression, and objective response rate. The aim of this analysis was to estimate the cost-effectiveness of lenvatinib versus sorafenib for the first-line treatment of patients with uHCC from a Canadian perspective. A cost-utility analysis was conducted using partitioned survival modelling, with health states representing progression-free disease, progressed disease, and death. Health effects were measured using quality-adjusted life years (QALYs), and costs were represented in Canadian dollars. Clinical inputs were derived from the REFLECT trial, with outcomes extrapolated using parametric survival models. EQ-5D data collected in REFLECT were used to determine health state utility values, and estimates of resource use came from a survey of clinicians. The model predicted incremental costs of-$5,021 and incremental QALYs of 0.17, making lenvatinib dominant over sorafenib. The model demonstrates lenvatinib to be a cost-effective use of resources versus sorafenib in Canada for the treatment of uHCC. Overall costs are lower compared with sorafenib, while health benefits are greater, with modelled progression-free and overall survival extended by 4.1 and 2.6 months in the lenvatinib arm, respectively.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".