Cost-Effectiveness of Sequential Therapy with Tumor Necrosis Factor Antagonists in Early Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To estimate the comparative lifetime cost-effectiveness of sequenced therapy with tumor necrosis factor (TNF) antagonists as the initial therapeutic intervention for patients with early rheumatoid arthritis (RA). METHODS: Because patients with RA switch regimens many times throughout the course of disease, sequenced therapeutic interventions were modeled, continuing until the last effective agent failed or death occurred. The model used published clinical outcomes from short-term, randomized controlled trials. Direct treatment costs and costs of lost productivity were modeled for each of 5 alternative treatment sequences. Incremental cost-effectiveness ratios are expressed as quality-adjusted lifeyears (QALY) gained. RESULTS: Treatment sequences that included TNF antagonists produced a greater number of QALY than conventional disease modifying antirheumatic drug regimens alone. The cost-effectiveness of sequenced therapy initiated with adalimumab plus methotrexate (MTX) extendedly dominated both infliximab-plus-MTX-initiated and etanercept sequences. The cost of adalimumab plus MTX per QALY was US $47,157 excluding productivity losses, and $19,663 including productivity losses. A supplementary sequence that incorporated adalimumab-plus-MTX-initiated first-line therapy followed by another TNF antagonist as second-line therapy was modeled; this sequence resulted in additional QALY gained and extendedly dominated all single-TNF strategies. CONCLUSION: Of the 3 single-TNF antagonist sequences, the adalimumab-plus-MTX-initiated sequence was cost-effective in producing the greatest number of QALY. Multiple TNF strategies, such as the supplementary sequence modeled in this analysis, may be cost-effective in producing even greater health gain.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".