Generalized meta-analysis for multiple regression models across studies with disparate covariate information.

ABSTRACT: Meta-analysis is widely popular for synthesizing information on common parameters of interest across multiple studies because of its logistical convenience and statistical efficiency. We develop a generalized meta-analysis approach to combining information on multivariate regression parameters across multiple studies that have varying levels of covariate information. Using algebraic relationships among regression parameters in different dimensions, we specify a set of moment equations for estimating parameters of a maximal model through information available from sets of parameter estimates for a series of reduced models from the different studies. The specification of the equations requires a reference dataset for estimating the joint distribution of the covariates. We propose to solve th

SUBMITTER: Kundu P 

PROVIDER: S-EPMC6690173 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature