Rituximab or cyclosporine A for the treatment of membranous nephropathy: economic evaluation of the MENTOR trial
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: The MENTOR trial (MEmbranous Nephropathy Trial Of Rituximab) showed that rituximab was noninferior to cyclosporine in inducing complete or partial remission of proteinuria and was superior in maintaining proteinuria remission. However, the cost of rituximab may prohibit first-line use for some patients and health-care payers. METHODS: A Markov model was used to determine the incremental cost-effectiveness ratio (ICER) of rituximab compared with cyclosporine for the treatment membranous nephropathy from the perspective of a health-care payer with a lifetime time horizon. The model was informed by data from the MENTOR trial where possible; additional parameters including cost and utility inputs were obtained from the literature. Sensitivity analyses were performed to evaluate the impact of reduced-cost biosimilar rituximab. RESULTS: Rituximab for the treatment of membranous nephropathy was cost effective (assuming a willingness-to-pay threshold of $50 000 per quality-adjusted life year (QALY) gained; in $US 2021) compared with cyclosporine, with an ICER of $8373/QALY over a lifetime time horizon. The incremental cost of rituximab therapy was $28 007 with an additional 3.34 QALYs compared with cyclosporine. Lower cost of rituximab biosimilars resulted in a more favorable ICER, and in some cases resulted in rituximab being dominant (lower cost and great benefit) compared to cyclosporine. CONCLUSIONS: Despite the greater cost of rituximab, it may be a cost-effective option for the treatment of membranous nephropathy when compared with cyclosporine. The cost-effectiveness of rituximab is further improved with the use of less expensive biosimilars.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".