Cost–utility of nelarabine for the first‐line treatment of newly diagnosed pediatric T‐cell acute lymphoblastic leukemia in Canada
Bibliographic record
Abstract
BACKGROUND: The Children's Oncology Group (COG)-AALL0434 trial investigated the addition of nelarabine to the augmented Berlin-Frankfurt-Münster (aBFM) protocol in patients (1.0-30.99 years) with newly diagnosed T-cell acute lymphoblastic leukemia (T-ALL). Despite demonstrating superior outcomes, nelarabine is not currently funded by many health systems, in part due to a lack of cost-effectiveness data. We estimated the cost-utility of nelarabine for this indication from a Canadian public healthcare payer perspective. METHODS: We developed a microsimulation model that followed hypothetical patients with newly diagnosed T-ALL from post-induction therapy to death. Three health states were modeled: relapse-free, post-relapse, and death. Efficacy was estimated using AALL0434 and retrospective data from Ontario, Canada. Costs were obtained from Canadian sources. Utility estimates and long-term mortality risks were sourced from literature. Total healthcare costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER) were reported. Probabilistic and scenario analyses were conducted. RESULTS: Incorporating nelarabine in the aBFM protocol increased costs by $51,670 Canadian dollars per patient, but resulted in 1.97 more QALYs and an ICER of $26,184/QALY. Most of the identified cost and benefit were accrued within the AALL0434 trial period (first 11 years post diagnosis) and while patients were in the relapse-free health state. Across multiple scenarios, the ICER was stable under an assumed $50,000/QALY threshold. CONCLUSION: Incorporating nelarabine into aBFM was cost-effective across different scenarios and assumptions. These results support its funding by public and private payers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".