Generalized Meta-Analysis for Multiple Regression Models Across Studies with Disparate Covariate Information

TL;DR

This paper introduces a generalized meta-analysis method for combining multivariate regression results from multiple studies with varying covariate data, using moment equations and GMM for estimation.

Contribution

It develops a novel framework that integrates disparate study data for multivariate regression, extending meta-analysis capabilities with a GMM-based approach and diagnostic tools.

Findings

Effective estimation of maximal model parameters across studies

Simulation studies demonstrate accuracy and robustness

Application to breast cancer risk prediction illustrates practical utility

Abstract

Meta-analysis, because of both logistical convenience and statistical efficiency, is widely popular for synthesizing information on common parameters of interest across multiple studies. We propose developing a generalized meta-analysis approach for combining information on multivariate regression parameters across multiple different studies which have varying level of covariate information. Using algebraic relationships between 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 from a series of reduced models available from the different studies. The specification of the equations requires a reference dataset to estimate the joint distribution of the covariates. We propose to solve these equations using the generalized method of moments approach, with the optimal weighting of the equations taking into account uncertainty associated with estimates of the parameters of the reduced models. We describe extensions of the iterated reweighted least square algorithm for fitting generalized linear regression models using the proposed framework. Based on the same moment equations, we also propose a diagnostic test for detecting violation of underlying model assumptions, such as those arising due to heterogeneity in the underlying study populations. Methods are illustrated using extensive simulation studies and a real data example involving the development of a breast cancer risk prediction model using disparate risk factor information from multiple studies.