Cost-Utility Analysis of Durvalumab and Tremelimumab Versus Best Supportive Care in Refractory Metastatic Colorectal Cancer
Bibliographic record
Abstract
OBJECTIVES: To determine the cost-effectiveness of combined durvalumab and tremelimumab in patients with metastatic colorectal cancer in the intention-to-treat (ITT) and biomarker-enriched populations using direct CCTG CO.26 phase-2 trial data. METHODS: A 4-state microsimulation model was used to evaluate the expected health outcomes in quality-adjusted life years (QALYs) and costs (2023 Canadian Dollars) over a lifetime horizon (5 years) from the Canadian public-payer perspective. Direct phase 2 CCTG CO.26 trial data informed model inputs, including overall survival Kaplan-Meier curves, progression-free survival Kaplan-Meier curves, and adverse event rates. Health-state utilities and costs of therapy, hospitalization, end-of-life care, sequencing panels, and physician care were obtained from published literature and Canadian costing databases. The incremental cost-utility ratios (ICURs) for the ITT and biomarker-enriched populations were determined. RESULTS: In the ITT population, expected QALYs for the treatment and best supportive care arms were 0.47 and 0.33 (incremental (Δ)0.14), respectively, and expected costs were $56 743 and $17 177 (Δ$39 566) for an ICUR of $277 661/QALY. In the plasma tumor mutation burden > 28 subgroup, expected QALYs were 0.43 and 0.21 (Δ0.21) and expected costs were $58 498 and $16 941 (Δ$41 557) for an ICUR of $193 945/QALY. CONCLUSIONS: Combined durvalumab and tremelimumab is not cost-effective in refractory metastatic colorectal cancer per conventional cost-effectiveness thresholds. Cost-effectiveness is more favorable in the high-plasma tumor mutation burden subgroup, but costs of screening and cutoffs used must be considered.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".