Computing within-study covariances, data visualization, and missing data solutions for multivariate meta-analysis with metavcov - Lu, Min (Frontiers in Psychology, 2023)
Multivariate meta-analysis (MMA) is a powerful statistical technique that can provide more reliable and informative results than traditional univariate meta-analysis, which allows for comparisons across outcomes with increased statistical power. However, implementing appropriate statistical methods for MMA can be challenging due to the requirement of various specific tasks in data preparation. The metavcov package aims for model preparation, data visualization and missing data solutions to provide tools to different methods that cannot be found in accessible software. It provides sufficient constructs for estimating coefficients from other well-established packages. For model preparation, users can compute both effect sizes of various types and their variance- covariance matrices, including correlation coefficients, standardized mean difference, mean difference, log odds ratio, log risk ratio, and risk difference. The package provides a tool to plot the confidence intervals for the primary studies and the overall estimates. When specific effect sizes are missing, single imputation is available in the model preparation stage; a multiple imputation method is also available for pooling the results in a statistically principled manner from models of users’ choice. The package is demonstrated in two real data applications and a simulation study to assess methods for handling missing data.