Biologic drugs for rheumatoid arthritis in the medicare program: A cost‐effectiveness analysis
Bibliographic record
Abstract
OBJECTIVE: Since the introduction of the Medicare Prescription Drug Improvement and Modernization Act and its associated demonstration project, coverage of selected biologic drugs has been expanded for Medicare beneficiaries. For rheumatoid arthritis, coverage was extended to etanercept, adalimumab, and anakinra in addition to the previously covered infliximab. We undertook to develop a model to compare the costs and quality-adjusted life years (QALYs) generated by each of the 4 biologic agents. METHODS: Data were drawn from meta-analysis of randomized controlled trials and from a large longitudinal outcomes databank. Uncertainty was addressed using probabilistic and one-way sensitivity analyses. A lifetime horizon and Medicare viewpoint were adopted. RESULTS: In the base case analysis, anakinra was the least effective and least costly strategy. Etanercept, adalimumab, and infliximab were similar in terms of effectiveness, but infliximab was more costly. If decision makers are willing to pay a maximum of $50,000/QALY, the probability that infliximab is cost-effective is <1%. Findings were robust to a range of sensitivity analyses. Only if the dose of infliximab remains constant over time is this likely to be a cost-effective strategy. CONCLUSION: Infliximab is unlikely to be cost-effective in the Medicare population compared with either etanercept or adalimumab. Anakinra is substantially less costly but is also less effective than the 3 tumor necrosis factor alpha inhibitors.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".