Cost‐Effectiveness Analysis of First‐Line Treatment With Biologic Agents in Polyarticular Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: The optimal timing of biologic agent treatment in polyarticular juvenile idiopathic arthritis (JIA) is unknown. This study evaluated the costs and outcomes of first-line treatment with etanercept (ETN), an anti-tumor necrosis factor (anti-TNF) agent, compared with step-wise therapy in JIA. METHODS: We compared 2 strategies: methotrexate (MTX) plus ETN as first-line therapy (ETN-first) and MTX monotherapy followed by ETN (ETN-second), using a cohort state-transition model of newly diagnosed JIA patients. The model's time horizon was 5 years, and the perspective was that of the Canadian health care system. The base case patient was 11 years old, weighed 40 kg, and had 5 or more active joints. Direct costs were calculated and discounted at a rate of 3% per year. Outcomes were expressed as quality-adjusted life years (QALYs). Scenario analyses varied multiple parameters simultaneously to model more severely and more mildly affected patients. RESULTS: ETN-first, compared to ETN-second, yielded a discounted incremental cost of $16,893 (95% confidence interval [95% CI] 9,348-25,310), incremental QALY of 0.19 (95% CI 0.08-0.32), and an incremental cost-effectiveness ratio of $88,815 per QALY gained. The results were sensitive to the cost of ETN, the time horizon of the model, and estimates of the efficacy of the first-line therapies. The cost per QALY for treating patients with severe JIA was $33,960. CONCLUSION: First-line therapy of ETN and MTX is relatively expensive compared to MTX alone, but may be economically attractive for more severely affected patients. More research is needed regarding the efficacy of first-line anti-TNF agents.
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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 | 0.012 |
| 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 | 0.000 |
| 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".