Economic Analysis of Alemtuzumab in Fludarabine-Refractory and Relapsed Chronic Lymphocytic Leukemia in Canada
Bibliographic record
Abstract
Abstract Background: Alemtuzumab (ALEM), a humanized antibody that recognizes the CD52 antigen, is indicated for the treatment of B-cell chronic lymphocytic leukemia (CLL) in patients who have been treated with alkylating agent and who had failed fludarabine therapy. The objective of this economic analysis was to determine the incremental cost-effectiveness of ALEM in patients with fludarabine-refractory and relapsed CLL from the perspective of a publicly funded health care system in Canada. ALEM was compared to accepted treatments for this patient population. Methods: A two-year time horizon was used. Effectiveness and adverse event information was obtained from an extensive literature review process. Resource utilization was based on guidelines, literature and expert opinion. Costs were obtained from provincial sources and presented in 2008 Canadian dollars. Only direct medical costs were considered. A number of comparators were used to represent standard treatment: salvage therapy, which included a variety of literature-based treatment options for fludarabine-refractory patients, fludarabine+cyclophosphamide (FC), flud arabine+cyclophosphamide+rituximab (FCR), and best supportive care (BSC). BSC included the palliative costs for caring for individuals previously treated with FC. Results: Mean survival for ALEM was 20.89 months (1.74 years), salvage therapy 16.32 months (1.36 years), FC 17.44 months (1.45 years), FCR 20.06 months (1.67 years). The incremental cost-effectiveness ratio (ICER) for the base case-ALEM vs. salvage therapy was Canadian $52,021/life year gained (LYG); ALEM vs. FC $68,165/LYG; ALEM vs. FCR $20,886/LYG. For ALEM vs. BSC, there was increased survival and cost savings with ALEM. Base case ICERs were sensitive to four variables: treatment duration of FC; treatment duration of FCR; treatment duration of ALEM; and additional survival due to rituximab for FCR patients compared to FC. Sensitivity analyses of the remaining variables showed minimal change to the base case ICERs. These variables were: ALEM administration (proportion of intravenous to subcutaneous); cytomegalovirus (CMV) complication rate for ALEM; discount rate; neutropenia and thrombocytopenia rates for ALEM; and neutropenia and thrombocytopenia rates for FCR. Conclusion: The results show that ALEM has a favourable ICER and provided good economic value for patients with fludarabine-refractory and relapsed CLL when compared to salvage therapy, FC and FCR. ALEM was found to be dominant over BSC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".