Importance of assessing and adjusting for cross-study heterogeneity in network meta-analysis: a case study of psoriasis
Bibliographic record
Abstract
AIM: The importance of adjusting for cross-study heterogeneity when conducting network meta-analyses (NMAs) was demonstrated using a case study of biologic therapies for moderate-to-severe plaque psoriasis. METHODS: Bayesian NMAs were conducted for Psoriasis Area and Severity Index 90 response. Several covariates were considered to account for cross-trial differences: baseline risk (i.e., placebo response), prior biologic use, body weight, psoriasis duration, age, race and baseline Psoriasis Area and Severity Index score. Model fit was evaluated. RESULTS: The baseline risk-adjusted NMA, which adjusts for multiple observed and unobserved effect modifiers, was associated with the best model fit. Lack of adjustment for cross-trial differences led to different clinical interpretations of findings. CONCLUSION: Failure to adjust for cross-trial differences in NMA can have important implications for clinical interpretations when studying the comparative efficacy of healthcare interventions.
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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 | 0.354 | 0.596 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.019 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".