Cost-Effectiveness Analysis in Canada of Dasatinib Versus Imatinib for the Treatment of Chronic Myelogenous Leukemia in Patients with Imatinib Resistance.
Bibliographic record
Abstract
Abstract Objective: To estimate the cost-utility of dasatinib versus high-dose imatinib (HDI, imatinib 800mg/day) for chronic myelogenous leukemia (CML) in patients with resistance to imatinib at standard doses. Methods: We adapted to the Canadian setting a Markov model with a monthly cycle length that followed hypothetical patients until death. Cost effectiveness was evaluated during three phases of CML: chronic (86% of Ontario patients in 2006), accelerated (9%) and blast crisis (5%). Efficacy of dasatinib and HDI (cytogenetic and hematologic response) and adverse events (AEs) were taken from randomized trials. Canadian costs of medications, laboratory tests, professional fees, hospitalizations, and AEs were obtained from published sources. Societal utility scores were estimated from a Canadian time trade-off study. Analyses were performed from the perspective of the Ontario Ministry of Health. Costs (2006 CDN$) and outcomes (QALYs) were discounted at 5% annually after the first year. Univariate and probabilistic sensitivity analyses were carried out to quantify uncertainty for the incremental cost-effectiveness ratios (ICERs). Results: Dasatinib dominated HDI in chronic phase patients with additional QALYs (3.92 vs. 3.47) and a lower lifetime cost ($379,678 vs. $444,934). The ICERs of dasatinib vs. HDI in accelerated and blast phase were $88,098/QALY and $173,922/QALY respectively. The higher cost/QALY of dasatinib in the advanced phases is explained primarily by the increased predicted survival of dasatinib patients and therefore the longer duration of drug therapy for these patients. Parameters with the greatest influence on results were the time horizon, drug costs and utility values. The interpretation of results remained robust in univariate and probabilistic sensitivity analyses. Conclusion: From an economic perspective dasatinib is an attractive treatment choice for the majority of imatinib resistant CML patients and provides better value for money than HDI.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".