Heterogeneity in Comparisons of Discontinuation of Tumor Necrosis Factor Antagonists in Rheumatoid Arthritis - A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: We did a systematic review of studies comparing discontinuation of tumor necrosis factor alpha (TNF) antagonists in rheumatoid arthritis (RA) patients, pooled hazard ratios and assessed clinical and methodological heterogeneity. METHODS: We searched MEDLINE and EMBASE until June 2015 for pairwise hazard ratios for discontinuing infliximab, etanercept, and adalimumab from cohorts of RA patients. Hazard ratios were pooled using inverse variance weighting and random effects estimates of the combined hazard ratio were obtained. Clinical and methodological heterogeneity was assessed using the between-subgroup I-square statistics and meta-regression. RESULTS: Twenty-four unique studies were eligible and large heterogeneity (I-square statistics > 50%) was observed in all comparisons. Type of data, location, and order of treatment (first or second line) modified the magnitude and direction of discontinuation comparing infliximab with either adalimumab or etanercept; however, some heterogeneity remained. No effect modifier was identified when adalimumab and etanercept were compared. CONCLUSION: Heterogeneity in studies comparing discontinuation of TNF antagonists in RA is partially explained by type of data, location, and order of treatment. Pooling hazard ratios for discontinuing TNF antagonists is inappropriate because largely unexplained heterogeneity was demonstrated when random effect estimates were calculated.
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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 | 0.038 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.046 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".