Health Technology Assessment Decision-Making Regarding Combination Therapy to Treat Advanced Hepatocellular Carcinoma: Comparison of Appraisals in Canada and the United Kingdom
Bibliographic record
Abstract
In 2019, atezolizumab plus bevacizumab (ATZ+BVA) became the first combination treatment to demonstrate a significant improvement in overall survival for patients with advanced hepatocellular carcinoma (HCC). To be reimbursed in publicly funded healthcare systems, ATZ+BVA was evaluated by national healthcare technology assessment (HTA) agencies, specifically the Canadian Agency for Drugs and Technologies in Health (CADTH) and the National Institute for Health and Care Excellence (NICE) in the United Kingdom (UK). This paper compares the clinical and economic research regarding ATZ+BVA for the treatment of advanced HCC that was considered by NICE and CADTH and the impact of these evidence on final public reimbursement recommendations. It also provides an HEOR evidence generation plan for tremelimumab plus durvalumab (TREM+DVA), a newly approved combination treatment for advanced HCC, to prepare for future HTA appraisals. Primary published literature on ATZ+BVA and the final reports issued by CADTH and NICE were used to identify clinical efficacy and cost-effectiveness evidence that were considered by the HTA agencies in their appraisals. Findings showed that both NICE and CADTH accepted phase 3 study data and an indirect treatment comparison to support the clinical efficacy of ATZ+BVA versus current treatment options. The primary reason for different funding recommendations for ATZ+BVA from NICE (full public reimbursement) versus CADTH (reimbursement with conditions) was the lack of cost-effectiveness in the Canadian model due to treatment cost. Therefore, manufacturers of new combination treatments for advanced HCC, like TREM+DVA, should competitively price their treatments to increase the likelihood of positive recommendations from NICE & CADTH, in addition to generating evidence on the real-world need for new treatments, clinical benefits versus all relevant comparators, and cost-effectiveness. However, it is important to note that recommendations made by HTA agencies should be interpreted and compared with caution as HTA appraisals do not necessarily reflect final funding decisions.
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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 | 0.099 | 0.331 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".