Joint Estimation of Remission and Response for Methotrexate‐Based DMARD Options in Rheumatoid Arthritis: A Bivariate Network Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: To jointly estimate American College of Rheumatology (ACR50) response (a more commonly reported outcome) and remission (a more clinically relevant outcome) for methotrexate (MTX)-based treatment options in rheumatoid arthritis (RA). METHODS: We conducted a bivariate network meta-analysis (NMA) to compare MTX monotherapy and MTX-based conventional and biologic disease-modifying antirheumatic drug (DMARD) combinations for RA. The correlation between the outcomes was derived from an incident RA cohort study, whereas the treatment effects were derived from randomized trials in the network of evidence. The analyses were conducted separately for MTX-naïve and MTX-inadequate response (IR) populations in a Bayesian framework with uninformative priors. RESULTS: From the cohort study, the correlation between ACR50 response and Disease Activity Score 28 remission at 6 months was moderate (Pearson correlation coefficient = 0.58). In the bivariate NMA for MTX-naïve populations, most combinations of MTX with either biologic or tofacitinib were statistically superior to MTX alone for both ACR50 response and remission. Triple therapy (MTX + sulfasalazine + hydroxychloroquine) was the only nonbiologic DMARD statistically superior to MTX for either ACR50 response (odds ratio [OR] 95% credible interval: 2.1 [1.0, 4.3]) or remission (OR: 2.5 [1.0, 5.8]). In the MTX-IR analysis, all treatments except MTX + sulfasalazine were statistically superior to MTX alone. Compared to analyzing the outcomes separately, the bivariate model often resulted in more precise estimates and allowed remission to be estimated for all treatments. CONCLUSION: Borrowing the strength of correlation between outcomes allowed us to demonstrate a statistically significant benefit for remission across most MTX-based DMARD combinations, including triple 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 | 0.058 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".