Cost-Effectiveness of Combination Nonbiologic Disease-Modifying Antirheumatic Drug Strategies in Patients with Early Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To compare the costs and benefits of alternative combination strategies of disease-modifying antirheumatic drugs (DMARD) and DMARD monotherapy in patients with early, active rheumatoid arthritis (RA). METHODS: Data were drawn from randomized controlled trials that compared DMARD monotherapy or any DMARD combination strategy, with or without combined steroid therapy. Mixed treatment comparison methods were used to estimate the relative effectiveness of the different strategies. A mathematical model was developed to compare the longterm costs and benefits of the alternative strategies, combining data from a variety of sources. Costs were considered from a health sector viewpoint and benefits were expressed in terms of quality-adjusted life-years (QALY). RESULTS: If decision makers use a threshold of £20,000 (US$29,000) per QALY, then the strategies most likely to be cost-effective are either DMARD combination therapy with downward titration (probability of being optimal = 0.50) or intensive, triple DMARD combination therapy (probability of being optimal = 0.43). The intensive DMARD strategy generated an additional cost of £27,392 per additional QALY gained compared to the downward titration strategy. Other combination strategies were unlikely to be considered cost-effective compared to DMARD monotherapy. Results were robust to a range of scenario sensitivity analyses. CONCLUSION: Combination DMARD therapy is likely to be cost-effective compared to DMARD monotherapy where treatment entails rapid downward dose titration or intensive, triple DMARD therapy.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.004 | 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".