Network meta‐interpolation: Effect modification adjustment in network meta‐analysis using subgroup analyses
Bibliographic record
Abstract
Effect modification (EM) may cause bias in network meta-analysis (NMA). Existing population adjustment NMA methods use individual patient data to adjust for EM but disregard available subgroup information from aggregated data in the evidence network. Additionally, these methods often rely on the shared effect modification (SEM) assumption. In this paper, we propose Network Meta-Interpolation (NMI): a method using subgroup analyses to adjust for EM that does not assume SEM. NMI balances effect modifiers across studies by turning treatment effect (TE) estimates at the subgroup- and study level into TE and standard errors at EM values common to all studies. In an extensive simulation study, we simulate two evidence networks consisting of four treatments, and assess the impact of departure from the SEM assumption, variable EM correlation across trials, trial sample size and network size. NMI was compared to standard NMA, network meta-regression (NMR) and Multilevel NMR (ML-NMR) in terms of estimation accuracy and credible interval (CrI) coverage. In the base case non-SEM dataset, NMI achieved the highest estimation accuracy with root mean squared error (RMSE) of 0.228, followed by standard NMA (0.241), ML-NMR (0.447) and NMR (0.541). In the SEM dataset, NMI was again the most accurate method with RMSE of 0.222, followed by ML-NMR (0.255). CrI coverage followed a similar pattern. NMI's dominance in terms of estimation accuracy and CrI coverage appeared to be consistent across all scenarios. NMI represents an effective option for NMA in the presence of study imbalance and available subgroup data.
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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 | 0.108 | 0.252 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.022 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".