Lenvatinib Versus Sorafenib as First-Line Treatment of Unresectable Hepatocellular Carcinoma: A Cost–Utility Analysis
Bibliographic record
Abstract
BACKGROUND: In a global, phase III, open-label, noninferiority trial (REFLECT), lenvatinib demonstrated noninferiority to sorafenib in overall survival and a statistically significant increase in progression-free survival in patients with unresectable hepatocellular carcinoma (HCC). Recently, lenvatinib became the first agent in more than 10 years to receive approval as first-line therapy for unresectable HCC, along with the previously approved sorafenib. The objective of this study was to determine the comparative cost-effectiveness of lenvatinib and sorafenib as a first-line therapy of unresectable HCC. MATERIALS AND METHODS: A state-transition model of unresectable HCC was developed in the form of a cost-utility analysis. The model time horizon was 5 years; the efficacy of the model was informed by the REFLECT trial, and costs and utilities were obtained from published literature. Probabilistic sensitivity analyses and subgroup analyses were performed to test the robustness of the model. RESULTS: Lenvatinib dominated sorafenib in the base case analysis. A probabilistic sensitivity analysis indicated that lenvatinib remains a cost-saving measure in 64.87% of the simulations. However, if the cost of sorafenib was reduced by 57%, lenvatinib would no longer be the dominant strategy. CONCLUSION: Lenvatinib offered a similar clinical effectiveness at a lower cost than sorafenib, suggesting that lenvatinib would be a cost-saving alternative in treating unresectable HCC. However, lenvatinib may fail to remain cost-saving if a significantly cheaper generic sorafenib becomes available. IMPLICATIONS FOR PRACTICE: This analysis suggests an actionable clinical policy that will achieve cost saving. This cost-utility analysis showed that lenvatinib had a similar clinical effectiveness at a lower cost than sorafenib, indicating that lenvatinib may be a cost-saving measure in patients with unresectable HCC, in which $23,719 could be saved per patient. The introduction of a new therapeutic option for the first time in 10 years in Canada provides an important opportunity for clinicians, researchers, and health care decision-makers to explore potential modifications in recommendations and practice guidelines.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".