Meta-analytic Gaussian Network Aggregation

Sacha Epskamp,Adela-Maria Isvoranu,Mike W.-L Cheung

doi:10.1007/s11336-021-09764-3

Sacha Epskamp, Adela-Maria Isvoranu + Show 1 more

Open Access

https://doi.org/10.1007/s11336-021-09764-3

Copy DOI

Abstract

A growing number of publications focus on estimating Gaussian graphical models (GGM, networks of partial correlation coefficients). At the same time, generalizibility and replicability of these highly parameterized models are debated, and sample sizes typically found in datasets may not be sufficient for estimating the underlying network structure. In addition, while recent work emerged that aims to compare networks based on different samples, these studies do not take potential cross-study heterogeneity into account. To this end, this paper introduces methods for estimating GGMs by aggregating over multiple datasets. We first introduce a general maximum likelihood estimation modeling framework in which all discussed models are embedded. This modeling framework is subsequently used to introduce meta-analytic Gaussian network aggregation (MAGNA). We discuss two variants: fixed-effects MAGNA, in which heterogeneity across studies is not taken into account, and random-effects MAGNA, which models sample correlations and takes heterogeneity into account. We assess the performance of MAGNA in large-scale simulation studies. Finally, we exemplify the method using four datasets of post-traumatic stress disorder (PTSD) symptoms, and summarize findings from a larger meta-analysis of PTSD symptom.

Highlights

A growing number of publications focus on estimating Gaussian graphical models (GGM, networks of partial correlation coefficients)
Extending the problem to multiple datasets, we introduce the meta-analytic Gaussian network aggregation (MAGNA) framework, which is derived from earlier work on multi-group structural equation modeling (SEM; Bollen and Stine 1993) and meta-analytic SEM
The first is a fully reproducible example of MAGNA analysis on a set of four datasets on post-traumatic stress disorder (PTSD) symptoms (Fried et al 2018), and the second is a description of a largescale meta-analysis using MAGNA, which we describe in more detail elsewhere (Isvoranu et al in press) Supplement 4 shows a second empirical example on a homogeneous set of datasets studying anxiety, depression and stress symptoms (Lovibond and Lovibond 1995), obtained from the Open Source Psychometrics Project

Summary

Introduction

A growing number of publications focus on estimating Gaussian graphical models (GGM, networks of partial correlation coefficients). While recent work emerged that aims to compare networks based on different samples, these studies do not take potential cross-study heterogeneity into account To this end, this paper introduces methods for estimating GGMs by aggregating over multiple datasets. A recent review indicated that, by the end of 2019, 141 studies in psychopathology have been published in which cross-sectional datasets were analyzed using network models, the majority of which used GGMs (Robinaugh et al 2020) These studies include high impact studies in diverse research fields, including post-traumatic stress disorder (PTSD; Mcnally et al 2015), psychosis (Isvoranu et al 2019), depression (Fried et al 2016), and personality research (Costantini et al 2015).

Objectives

Findings

Discussion

Conclusion