SynthesisPsychometrika2022
Meta-analytic Gaussian Network Aggregation.
Synthesis in Psychometrika, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A network model of depressive and anxiety symptoms: a statistical evaluation.Molecular psychiatry · 2024Pooled it
- Network-based methods for psychometric data of eating disorders: A systematic review.PloS one · 2022Pooled it
- Psychological Network of Stress, Coping and Social Support in an Aboriginal Population.International journal of environmental research and public health · 2022Trial
- Association between information overload, asthenopia, and depressive and anxiety symptoms in patients with lung cancer: A computer-simulated network analysis.Asia-Pacific journal of oncology nursing · 2026Article
- From review to synthesis: A step-by-step methodological guide to systematic reviews and multilevel meta-analyses.Behavior research methods · 2026Article
- Network psychometrics in practice: A practical framework for designing empirical studies that utilize a psychological network approach.Behavior research methods · 2026Article
- Statistical evidence in psychological networks.Nature human behaviour · 2026Article
- Lagged symptom-level associations among pain, sleep disturbance, and depressive-affective symptoms in adults aged ≥50 years: an international longitudinal multi-cohort study.Frontiers in medicine · 2026Article
- Bayes Factor Tests for Group Differences in Ordinal and Binary Graphical Models.Psychometrika · 2025Article
- The psychometric network of individual flourishing across nationally representative samples from 22 countries.Scientific reports · 2025Article
- Multisystemic resilience and its impact on youth mental health: reflections on co-designing a multi-disciplinary, participatory study.Frontiers in child and adolescent psychiatry · 2025Article
- Network analysis: An overview for mental health research.International journal of methods in psychiatric research · 2024Review
- Stress response and experiential avoidance among firefighters: Preliminary insights from network analyses.Journal of psychiatric research · 2023Article
- SpiderLearner: An ensemble approach to Gaussian graphical model estimation.Statistics in medicine · 2023Article
- A Cross-Country Network Analysis of Resilience Systems in Young Adults.Emerging adulthood (Print) · 2023Article
- Better together: novel methods for measuring and modeling development of executive function diversity while accounting for unity.Frontiers in human neuroscience · 2023Article
- Gaussian graphical models with applications to omics analyses.Statistics in medicine · 2022Article
- Generalized network psychometrics of eating-disorder psychopathology.The International journal of eating disorders · 2022Article
- A network approach to relationships between cannabis use characteristics and psychopathology in the general population.Scientific reports · 2022Article
- Possible Futures for Network Psychometrics.Psychometrika · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.