The cost effectiveness of lenvatinib versus atezolizumab and bevacizumab or sorafenib in patients with unresectable hepatocellular carcinoma (uHCC) in Canada.
Bibliographic record
Abstract
4098 Background: Hepatocellular carcinoma (HCC) represents 72% of liver cancers in Canada. In the phase III REFLECT trial, lenvatinib met the primary endpoint of non-inferiority in overall survival (OS) versus sorafenib and demonstrated superiority in secondary endpoints of progression free-survival (PFS), time to progression and objective response rate. Based on the REFLECT trial, lenvatinib has become the standard of care in the treatment of Canadian patients with unresectable HCC (uHCC). In the Phase III IMbrave150 trial, the use of the combination of atezolizumab and bevacizumab (atezo+bev) resulted in statistically significant improvement in OS and PFS versus sorafenib for patients with uHCC. The aim of this analysis was to estimate the cost-effectiveness of lenvatinib versus atezo+bev or sorafenib as first-line treatment for patients with uHCC from the perspective of Ministry of Health in Canada. Methods: A cost-utility analysis was conducted using a partitioned survival analysis. Health state membership for lenvatinib and sorafenib were estimated based on patient level data and clinical inputs from REFLECT and extrapolated using parametric survival models. Relative efficacy for atezo+bev was estimated from a de novo network meta-analysis. In the base-case analysis, estimates from REFLECT used in the NMA were adjusted for imbalances in baseline characteristics. Sensitivity analyses included the use of alternative approaches to determine relative efficacy. Health state utility values were determined with EQ-5D data collected in REFLECT. Drug acquisition costs were obtained from publicly available sources and medical resource utilization was based on a survey of Canadian clinicians. The time horizon was 10 years. Results of the incremental analysis were calculated sequentially. Results: In the base case lenvatinib was associated with cost savings of CAD$4,640 and CAD$120,095 and a QALY difference of 0.15 and -0.28 vs sorafenib and atezo+bev, respectively. The base case deterministic analysis resulted in lenvatinib being dominant over sorafenib and cost-effective vs. atezo+bev (sequential ICER for atezo+bev was CAD$425,754 per QALY). Results of the probabilistic sensitivity analysis (PSA) were consistent with the base case findings with lenvatinib being the optimal treatment strategy in >99% of iterations. Conclusions: Results of this analysis demonstrate that lenvatinib represents the optimal use of healthcare resources as a first-line treatment for uHCC in Canada.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".