Cost effectiveness of adding leflunomide to a 5‐year strategy of conventional disease‐modifying antirheumatic drugs in patients with rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: To estimate, from a public payer's perspective, the 5-year cost effectiveness of adding leflunomide (LEF) to a sequence of disease-modifying antirheumatic drugs (DMARDs) representative of a typical rheumatoid arthritis (RA) management approach adopted by Canadian rheumatologists. METHODS: A DMARD sequence including LEF was compared with one excluding it, using a 5-year simulation model where patients with RA cycle through different treatment regimens. Data were obtained through a systematic literature search (drug withdrawal rates, number and type of adverse events, American College of Rheumatology 20% responder status) and separately conducted surveys (choice of DMARD sequence, management of adverse events). Costs for adverse event management were calculated using the Ontario Schedule of Benefits, and monitoring costs were calculated according to official Canadian product monograph recommendations. Wholesale prices of all drugs were adjusted by the allowable markup and prescription fees. Utilities (as measured by the standard gamble [SG] and rating scale [RS] techniques) were obtained from 482 patients who participated in a 1-year randomized controlled trial that compared LEF, methotrexate, and placebo. Costs and utilities were discounted by 3%. Probabilistic sensitivity analysis was performed. RESULTS: Adding LEF to a conventional strategy of DMARDs increased the 5-year management costs by $1,231 compared with the strategy without LEF, which results in a cost-effectiveness ratio of $13,096 per additional year of response to treatment, and cost-utility ratios of $54,229 (RS) and $71,988 (SG) per quality-adjusted life-year gained. CONCLUSION: Adding LEF as a new option to a conventional sequence of DMARDs extends the time patients may benefit from DMARD therapy at a reasonable cost effectiveness and cost utility. LEF data are limited to clinical trials; data from observational studies would be needed to corroborate these findings.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".