B0445
Title: Cluster-robust estimators for multivariate mixed-effects meta-regression
Authors: Thilo Welz - TU Dortmund University (Germany) [presenting]
Wolfgang Viechtbauer - Maastricht University (Netherlands)
Markus Pauly - Technical University of Dortmund (Germany)
Abstract: Meta-analyses frequently include trials that report multiple outcomes based on a common set of study participants. These outcomes will generally be correlated. Cluster-robust variance-covariance estimators are a fruitful approach for synthesizing these dependent outcomes. However, when the number of studies is small, statistical tests regarding the model coefficients based on state-of-the-art robust estimators can have inflated type 1 error rates. Therefore, two new cluster-robust estimators are presented, in order to improve small sample performance. For both estimators, the idea is to transform the estimated variances of the residuals using only the diagonal entries of the hat matrix. The proposals are asymptotically equivalent to previously suggested cluster-robust estimators, such as the bias-reduced linearization approach. The methods are compared and contrasted based on empirical coverage of confidence regions for the coefficient vector $\beta$ in a Monte Carlo simulation study. The focus is on bivariate meta-regression with a single covariate.
