What drives the comparative effectiveness of biologics vs methotrexate in rheumatoid arthritis? Meta-regression and graphical inspection of suspected clinical factors
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to explore which clinical factors and patient characteristics are associated with the magnitude of comparative efficacy between biologics vs. MTX in RA patients with inadequate response to MTX. METHODS: We included randomized controlled trials assessing the efficacy of a biologic plus MTX vs. MTX alone. We examined several clinical factors and patient characteristics potentially associated with magnitude of response, measured as ACR20 (20% improvement in ACR criteria) and ACR50 (16-26 weeks). We employed meta-regression for formal estimates and statistical significance of effect modification. We produced regression and forest plots to further inspect potential associations. RESULTS: For ACR50, a 1-year increment on the average patient disease duration was statistically significantly associated with a 16% relative increase in the pooled odds ratio (OR) estimate (P = 0.003). A 1-year increment in patient age and a 1 mg/week increment in MTX dose were marginally statistically significantly associated with a 9% (P = 0.056) and 22% (P = 0.092) relative increase in the OR. For ACR20, the average number of swollen and tender joints was marginally statistically associated with a 3% relative decrease. The associations for age and MTX dose appeared to be partly driven by significant negative associations between these two factors and the control group response. CONCLUSION: Our analyses identified key variables associated with the magnitude of comparative effects for ACR outcomes. Our findings provide valuable insights for future trial designs and systematic reviews as well as decision-making and clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.173 | 0.225 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.068 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".