Economic Model to Evaluate the Cost-Effectiveness of Second-Line Nilotinib Versus Dasatinib for the Treatment of Philadelphia Chromosome-Positive Chronic Myeloid Leukemia (CML-CP) in Italy
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the cost effectiveness of second-line nilotinib versus dasatinib for the treatment of Philadelphia chromosome-positive chronic myeloid leukemia (CML-CP) patients who are intolerant or resistant to imatinib and can transition to treatment-free remission (TFR). METHODS: A partitioned survival model was developed to compare the cost effectiveness of nilotinib versus dasatinib. The model was developed from the Italian healthcare payer perspective and included the following health states: on second-line tyrosine kinase inhibitor (TKI), off second-line TKI, accelerated phase/blastic crisis, TFR, and death. Progression-free and overall survival curves were derived from patient-level data that compared nilotinib and dasatinib as second-line therapy in CML-CP patients who were resistant or intolerant to imatinib. Drug costs, healthcare costs, and adverse event costs were based on real-world evidence and publicly available databases. Cost effectiveness was estimated over a 40-year time horizon. Scenario analyses were performed by adjusting time horizon, TFR parameters, costs, and utilities. RESULTS: Second-line nilotinib resulted in greater time spent in TFR (0.91 life-years), increased quality-adjusted life-years (QALYs) (1.89), increased life-years (2.16), and decreased per-patient costs (- 38,760 €). Therefore, nilotinib was strongly dominant compared with dasatinib in the base-case analysis. Nilotinib remained strongly dominant in most scenario analyses including shorter time horizon, exclusion of TFR, and varying TKI drug costs. CONCLUSIONS: While the model showed that nilotinib treatment of imatinib-intolerant or resistant CML-CP patients was more effective and less costly than dasatinib treatment, there is considerable uncertainty in the findings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".