Cost‐effectiveness analysis of potentially curative and combination treatments for hepatocellular carcinoma with person‐level data in a Canadian setting
Bibliographic record
Abstract
Patients with early-stage hepatocellular carcinoma (HCC) are potential candidates for curative treatments such as radiofrequency ablation (RFA), surgical resection (SR), or liver transplantation (LT), which have demonstrated a significant survival benefit. We aimed to estimate the cost-effectiveness of curative and combination treatment strategies among patients diagnosed with HCC during 2002-2010. This study used Ontario Cancer Registry-linked administrative data to estimate effectiveness and costs (2013 USD) of the treatment strategies from the healthcare payer's perspective. Multiple imputation by logistic regression was used to handle missing data. A net benefit regression approach of baseline important covariates and propensity score adjustment were used to calculate incremental net benefit to generate incremental cost-effectiveness ratio (ICER) and uncertainty measures. Among 2,222 patients diagnosed with HCC, 10.5%, 14.1%, and 10.3% received RFA, SR, and LT monotherapy, respectively; 0.5-3.1% dual treatments; and 0.5% triple treatments. Compared with no treatment (53.2%), transarterial chemoembolization (TACE) + RFA (average $2,465, 95% CI: -$20,000-$36,600/quality-adjusted life years [QALY]) or RFA monotherapy ($15,553, 95% CI: $3,500-$28,500/QALY) appears to be the most cost-effective modality with lowest ICER value. The cost-effectiveness acceptability curve showed that if the relevant threshold was $50,000/QALY, RFA monotherapy and TACE+ RFA would have a cost-effectiveness probability of 100%. Strategies using LT delivered the most additional QALYs and became cost-effective at a threshold of $77,000/QALY. Our findings found that TACE+ RFA dual treatment or RFA monotherapy appears to be the most cost-effective curative treatment for patients with potential early stage of HCC in Ontario. These findings highlight the importance of identifying and measuring differential benefits, costs, and cost-effectiveness of alternative HCC curative treatments in order to evaluate whether they are providing good value for money in the real world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".