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.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".