Cost–utility analysis of second-line therapy with rituximab compared to tumour necrosis factor inhibitors in rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: To compare direct costs and treatment utility associated with the second-line therapy with rituximab and tumour necrosis factor inhibitors (TNFis) (adalimumab, etanercept, and infliximab) in patients with Rheumatoid Arthritis (RA) using data from a prospective registry. METHODS: = 129) who had to discontinue a first-line TNFi and were treated with rituximab, adalimumab, etanercept, or infliximab as the second-line therapy between January 2007 and May 2016. A decision analytic model followed patients for 1 and 6 years. Treatment utility was measured as quality-adjusted life-years (QALYs) gained, which were calculated from HAQ-DI scores observed over the follow-up time. Quebec 2020 unit costs (Canadian Dollars, $) were used to value healthcare resource consumption. A probabilistic sensitivity analysis was performed with 10,000 Monte Carlo simulations to assess uncertainty around point-estimates of cost-utility. RESULTS: Over 1-year, rituximab and etanercept resulted in the effectiveness of 0.80 QALYs gained at the cost of $14,291and $18,880, respectively, and were dominant (i.e. associated with lower costs and more QALYs gained) compared to adalimumab (0.79 QALYs, $18,825) and infliximab (0.76 QALYs, $20,158). Over 6-years, rituximab (4.42 QALYs, $82,402) was dominant compared to adalimumab (4.30 QALYs, $101,420), etanercept (4.02 QALYs, $99,191), and infliximab (3.71 QALYs, $100,396). In the probabilistic analysis, rituximab was dominant over adalimumab, etanercept, and infliximab with the probability of 0.51, 0.62, and 0.65, respectively. CONCLUSION: Real-world data revealed differences between alternative biologic agents used as the second-line therapy in terms of both treatment costs for the healthcare system and utility of treatment for patients. Therefore, new guidelines on the order of selecting and switching biologic agents should be explored.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".