A Multivariate Meta-Analysis Model for High-Dimensional Data
Description
Performs multivariate meta-analysis for high-dimensional data to integrate and collectively analyse individual-level data from multiple studies, as well as to combine summary estimates. This approach accounts for correlation between outcomes, incorporates within‑ and between‑study variability, handles missing values, and uses shrinkage estimation to accommodate high dimensionality. The 'MetaHD' R package provides access to our multivariate meta-analysis approach, along with a comprehensive suite of existing meta-analysis methods, including fixed-effects and random-effects models, Fisher’s method, Stouffer’s method, the weighted Z method, Lancaster’s method, the weighted Fisher’s method, and vote-counting approach. A detailed vignette with example datasets and code for data preparation and analysis is available at .