Meta-analytic Gaussian Network Aggregation

Epskamp, Sacha; Isvoranu, Adela‐Maria; Cheung, Mike W.‐L.

doi:10.31234/osf.io/236w8

Cited by 22 publications

(46 citation statements)

References 56 publications

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“…All statistical analyses were conducted in the R statistical software version 4.1.0 (R Core Team, 2015), using the MAGNA framework implemented in the R package psychonetrics version 0.7.5. (Epskamp, 2020). We employed a random-effects MAGNA model using an averaged individual estimate of the sampling variation matrix.…”

Section: Discussionmentioning

confidence: 99%

“…That is, if a network is estimated from a single sample, would similar conclusions be drawn from the resulting network structure? To this end, for each correlation matrix (Pearson correlations obtained through listwise deletion), we estimated network models using four techniques that allow for correlation matrices to be used as input: (1) the EBICglasso algorithm (Epskamp & Fried, 2018), which combines the graphical LASSO (Friedman et al, 2008) regularization with model selection using the extended Bayesian information criterion (EBIC; Foygel & Drton, 2010) and is estimated through using the EBICglasso function in the qgraph package (Epskamp et al, 2012), (2) unthresholded partial correlation estimation using the qgraph package, (3) the ggmModSelect algorithm using the qgraph package (Isvoranu et al, 2019), which performs extensive stepwise unregularized model search, and (4) pruning at = 0.05 using the psychonetrics package (Epskamp, 2020;, which is similar to the method used for estimating the pooled network structure except that it is only applied to a single study and does not take study-heterogeneity into account. We compared how well on average parameter estimates, as well as model selection compare to the pooled MAGNA network.…”

Section: Conflict Of Interestmentioning

confidence: 99%

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Network Models of Post-traumatic Stress Disorder: A Meta-analysis

Isvoranu¹,

Epskamp²,

Cheung³

2020

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Post-traumatic stress disorder (PTSD) researchers have increasingly used psychological network models to investigate PTSD symptom interactions, as well as to identify central driver symptoms. It is unclear, however, how generalizable such results are. We have developed a meta-analytic framework for aggregating network studies while taking between-study heterogeneity into account, and applied this framework to the first-ever meta-analytic study of network models. We analyzed the correlational structures of 52 different samples with a total sample size of n = 29,561, and estimated a single pooled network model underlying the datasets, investigated the scope of between-study heterogeneity, and assessed the performance of network models estimated from single studies. Our main findings are that: (1) While several clear symptom-links and interpretable clusters can be identified in the network, most symptoms feature very similar levels of centrality. To this end, aiming to identify central symptoms in PTSD symptom networks may not be fruitful. (2) We identified large between-study heterogeneity, indicating that it should be expected for networks of single studies to not perfectly align with one-another, and meta-analytic approaches are vital for the study of PTSD networks. (3) Nonetheless, we found evidence that networks estimated from single studies may give rise to generalizable results, as our results aligned with previous descriptive analyses of reported network studies, and network models estimated from single samples lead to similar network structures as the pooled network model. We discuss the implications of these findings for both the PTSD literature as well as methodological literature on network psychometrics.

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Section: Discussionmentioning

confidence: 99%

Section: Conflict Of Interestmentioning

confidence: 99%

Network Models of Post-traumatic Stress Disorder: A Meta-analysis

Isvoranu¹,

Epskamp²,

Cheung³

2020

Preprint

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“…Specifically, the sample sizes used when estimating each pairwise correlation separately are computed, and the average of these is taken as the final sample size in the analyses. Second, the psychonetrics package includes full information maximum likelihood estimation (Epskamp, Isvoranu, & Cheung, 2020), which will only use observed data to estimate the GGM structure.…”

Section: Unstable Network Structuresmentioning

confidence: 99%

Reporting Standards for Psychological Network Analyses in Cross-sectional Data

Burger¹,

Isvoranu²,

Lunansky³

et al. 2020

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Statistical network models describing multivariate dependency structures in psychological data have gained increasing popularity. Such comparably novel statistical techniques require specific guidelines to make them accessible to the research community. In this literature, researchers have previously provided tutorials guiding the estimation of networks and their accuracy. However, there is currently little guidance in determining what parts of the analyses and results should be documented in a scientific report. A lack of such reporting standards may foster researcher degrees of freedom and could provide fertile ground for questionable reporting practices. Here, we introduce reporting standards for network analyses in cross-sectional data, along with a tutorial and two examples. The presented guidelines are aimed at researchers as well as the broader scientific community, such as reviewers and journal editors evaluating scientific work. We conclude by discussing how the network literature specifically can benefit from such guidelines for reporting and transparency.

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“…This section describes how we generated data used in the simulation study. Given a GGM structure encoded in a square matrix (a matrix with zeroes on the diagonal and partial correlation coefficients on the off-diagonal elements), we first transformed this matrix into an expected correlation matrix using the following expression (Epskamp et al, 2020;Epskamp, Rhemtulla, et al, 2017):…”

Section: Fundingmentioning

confidence: 99%

Which Estimation Method to Choose in Network Psychometrics? Deriving Guidelines for Applied Researchers

Isvoranu¹,

Epskamp²

2021

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The Gaussian Graphical Model (GGM) has recently grown popular in psychological research, with a large body of estimation methods being proposed and discussed across various fields of study, and several algorithms being identified and recommend as applicable to psychological datasets. Such high-dimensional model estimation, however, is not trivial, and algorithms tend to perform differently in different settings. In addition, psychological research poses unique challenges, including placing a strong focus on weak edges (e.g., bridge edges), handling data measured on ordered scales, and relatively limited sample sizes. As a result, there is currently no consensus regarding which estimation procedure performs best in which setting. In this large-scale simulation study, we aimed to overcome this gap in the literature by comparing the performance of several estimation algorithms suitable for gaussian and skewed ordered categorical data across a multitude of settings, as to arrive at concrete guidelines from applied researchers. In total, we investigated 60 different metrics across 564,000 simulated datasets. We summarized our findings through a platform that allows for manually exploring simulation results. Overall, we found that an exchange between discovery (e.g., sensitivity, edge weight correlation) and caution (e.g., specificity, precision) should always be expected and achieving both¬—which is a requirement for perfect replicability—is difficult. Further, we identified that the estimation method is best chosen in light of each research question and highlighted, alongside desirable asymptotic properties and low sample size discovery, results according to most common research questions in the field.

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Meta-analytic Gaussian Network Aggregation

Cited by 22 publications

References 56 publications

Network Models of Post-traumatic Stress Disorder: A Meta-analysis

Network Models of Post-traumatic Stress Disorder: A Meta-analysis

Reporting Standards for Psychological Network Analyses in Cross-sectional Data

Which Estimation Method to Choose in Network Psychometrics? Deriving Guidelines for Applied Researchers

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