Economic Evaluation of the MENTOR Trial Comparing Rituximab and Cyclosporine for the Treatment of Membranous Nephropathy (MN)
Bibliographic record
Abstract
Background: The MENTOR trial (MEmbranous Nephropathy Trial Of Rituximab) showed that rituximab (RTX) was noninferior to cyclosporine (CSA) in inducing complete or partial remission of proteinuria and was superior in maintaining proteinuria remission. However, the cost of RTX is high and it's cost-effectiveness has not been determined. Methods: A Markov model (Fig 1) was used to determine the incremental cost-effectiveness ratio (ICER) of RTX compared with CSA for the treatment MN from the perspective of a health care payer with a life-time time horizon ($2020 USD). The model outcomes were informed by data from the MENTOR trial and previously published literature. Cost and utility inputs were obtained from the literature.Figure 1:: Diagram of health states and possible transitions in the Markov model.Results: Based on 1,000 simulations, the mean additional cost of RTX therapy for MN compared with CSA was $168,064 with an improvement in utility of 6.70 QALYs (Fig 2). RTX was cost-effective (assuming a willingness-to-pay threshold of $50,000 / QALY) compared with cyclosporine, with an ICER of $25,071 per additional quality adjusted life year (QALY) over a lifetime time horizon (45 years).Figure 2:: Results of the base-case model comparing the cost-effectiveness of rituximab and cyclosporine.Conclusions: While the initial cost of RTX is high, RTX is a cost-effective option (assuming willingness to pay thresholds of $50,000 or greater) for the treatment of MN when compared with the alternative of CSA. The cost-effectiveness will be further improved with the use of less expensive biosimilars.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".