Cost-effectiveness analysis of treatment with non-curative or palliative intent for hepatocellular carcinoma in the real-world setting
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) presentation is heterogeneous necessitating a variety of therapeutic interventions with varying efficacies and associated prognoses. Poor prognostic patients often undergo non-curative palliative interventions including transarterial chemoembolization (TACE), sorafenib, chemotherapy, or purely supportive care. The decision to pursue one of many palliative interventions for HCC is complex and an economic evaluation comparing these interventions has not been done. This study evaluates the cost-effectiveness of non-curative palliative treatment strategies such as TACE alone or TACE+sorafenib, sorafenib alone, and non-sorafenib chemotherapy compared with no treatment or best supportive care (BSC) among patients diagnosed with HCC between 2007 and 2010 in a Canadian setting. Using person-level data, we estimated effectiveness in life years and quality-adjusted life years (QALYs) along with total health care costs (2013 US dollars) from the health care payer's perspective (3% annual discount). A net benefit regression approach accounting for baseline covariates with propensity score adjustment was used to calculate incremental net benefit to generate incremental cost-effectiveness ratio (ICER) and uncertainty measures. Among 1,172 identified patients diagnosed with HCC, 4.5%, 7.9%, and 5.6%, received TACE alone or TACE+sorafenib, sorafenib, and non-sorafenib chemotherapy clone, respectively. Compared with no treatment or BSC (81.9%), ICER estimates for TACE alone or TACE+sorafenib was $6,665/QALY (additional QALY: 0.47, additional cost: $3,120; 95% CI: -$18,800-$34,500/QALY). The cost-effectiveness acceptability curve demonstrated that if the relevant threshold was $50,000/QALY, TACE alone or TACE+sorafenib, non-sorafenib chemotherapy, and sorafenib alone, would have a cost-effectiveness probability of 99.7%, 46.6%, and 5.5%, respectively. Covariates associated with the incremental net benefit of treatments are age, sex, comorbidity, and cancer stage. Findings suggest that TACE with or without sorafenib is currently the most cost-effective active non-curative palliative treatment approach to HCC. Further research into new combination treatment strategies that afford the best tumor response is needed.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".