Estimating Common Parameters in Heterogeneous Random Effects Models
Andrew L. Rukhin
A question of fundamental importance for meta-analysis of heterogeneous data studies is how to form a best consensus estimator of common parameters, and what uncertainty to attach to the estimate. This issue is addressed for a class of unbalanced linear designs which include classical growth curve models. The obtained solution is similar to the DerSimonian and Laird (1986) popular method for a simple meta-analysis model. By using almost unbiased variance estimators, an estimator of the covariance matrix of this procedure is derived. These methods are illustrated by two examples and are compared via simulation.