Bridging Statistical Rigor and Clinical Usability: The CORMeta App for Meta-Analysis of Correlated Outcomes
Bibliographic record
Abstract
Background: In clinical research, multiple outcomes are often measured within the same cohort, leading to statistical dependencies that violate assumptions of traditional meta-analytic methods. While advanced models can accommodate such correlations, they typically require programming expertise, limiting accessibility for many physician-researchers. Objective: We present a user-friendly, interactive Shiny web application designed to perform meta-analyses of correlated outcomes, with particular relevance for cohort-based clinical datasets. Methods: The application implements a modified multivariate meta-analytic framework that accounts for the correlation structure of outcomes within cohorts. Users can upload their data, define correlation matrices, and filter observations by any variable (e.g., age, domain, exposure) without writing code. The application provides graphical output (forest plots) along with estimates of overall effect size, heterogeneity (τ²), and p-values. Results: A demonstration dataset on prenatal alcohol exposure and neurodevelopmental outcomes is simulated to illustrate the application’s functionality. The application automatically generates correlation matrices where needed, adjusts for intra-cohort dependencies, and produces interpretable results suitable for clinical research reports. Conclusion: This open-access application bridges the gap between complex statistical modeling and clinical usability. It enables physicians to conduct robust meta-analyses of correlated outcomes with ease, supporting evidence-based practice and local research initiatives. The tool is particularly valuable in multi-domain or multi-cohort studies where outcome correlation is non-negligible.
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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 | 0.032 | 0.122 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.084 | 0.016 |
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".