Network analysis of PTSD and depressive symptoms in 158,139 treatment‐seeking veterans with PTSD

Duek, Or; Spiller, Tobias R.; Pietrzak, Robert H.; Fried, Eiko I.; Harpaz‐Rotem, Ilan

doi:10.1002/da.23112

Cited by 48 publications

(42 citation statements)

References 42 publications

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“…The strongest associations were identified between the symptoms "hypervigilant" and "easily startled", "internal avoidance" and "external avoidance", "emotional numbing" and "feeling detached", as well as "feeling detached" and "loss of interest". These results aligned well with results from a previously reported review on PTSD symptom network (Birkeland et al, 2020), with a recent analysis of a large sample of veterans (Duek et al, 2020), as well as with the example described in Section 7.1, which analyzes a subset of the larger dataset discussed in Isvoranu et al (2020).…”

Section: Empirical Applicationsupporting

confidence: 89%

Meta-analytic Gaussian Network Aggregation

Epskamp¹,

Isvoranu²,

Cheung³

2020

Preprint

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A growing number of publications focuses on estimating Gaussian graphical models (GGMs, 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 through 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 exemplify the method using four datasets of post-traumatic stress disorder symptoms, as well as one large dataset of depression, anxiety and stress symptoms. Finally, we assess the performance of MAGNA in large-scale simulation studies.

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Section: Empirical Applicationsupporting

confidence: 89%

Meta-analytic Gaussian Network Aggregation

Epskamp¹,

Isvoranu²,

Cheung³

2020

Preprint

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“…However, our findings suggest that caution is needed when interpreting specific associations in networks and when interpreting differences in the magnitude of associations within a single cross-sectional network. It is worth noting that we identified few differences in comparing the networks, suggesting that associations remained relatively stable even across cross-sectional samples (as has been found by other researchers, for instance in the context of post-traumatic stress disorder, Duek et al, 2021 ).…”

Section: Discussionsupporting

confidence: 78%

Exploratory and Confirmatory Bayesian Networks Identify the Central Role of Non-judging in Symptoms of Depression

Rubin¹,

Papini²,

Dainer-Best

et al. 2021

Mindfulness

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Objectives Depression is a highly heterogeneous disorder, and meta-analyses of mindfulness-based interventions show moderate efficacy for reducing depressive symptoms. However, the mechanisms governing their efficacy remain unclear, highlighting the need for hypothesis-generating analyses to guide future research. Methods We used Bayesian network analysis in three cross-sectional samples (N = 1135) of undergraduates and participants from the community to identify links between individual symptoms of depression and specific facets of mindfulness. In two exploratory studies, we assessed depression using the Patient Health Questionnaire ( n = 384) or the Depression Anxiety and Stress Scale ( n = 350) and mindfulness using the Five-Facet Mindfulness Scale. Results Across these samples and measures, exploratory analyses indicated that non-judging was a central bridge between facets of mindfulness and symptoms of depression. We confirmed this finding in a pre-registered replication ( n = 401) using a recently developed confirmatory testing framework for network analysis. Non-judging was consistently a central bridge in the networks and specifically linked to the symptoms of depression related to feelings of failure and worthlessness. Conclusions These findings provide strong evidence that non-judging is an essential feature of mindfulness in the context of depression and provides direction for future research testing mindfulness-oriented treatment prescriptions for depression. Supplementary Information The online version contains supplementary material available at 10.1007/s12671-021-01726-1.

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“…4. (Duek et al 2021), as well as with the example described in Sect. 7.1, which analyzes a subset of the larger dataset discussed in (Isvoranu et al in press) In terms of centrality-descriptive statistics often used in network analysis in attempts to identify the most influential nodes (Bringmann et al 2019)-the extended meta-analysis identified especially the symptoms "feeling detached".…”

Section: Empirical Application: a Meta-analysis Of 52 Ptsd Datasetsmentioning

confidence: 93%

Meta-analytic Gaussian Network Aggregation

2021

View full text Add to dashboard Cite

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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Network analysis of PTSD and depressive symptoms in 158,139 treatment‐seeking veterans with PTSD

Cited by 48 publications

References 42 publications

Meta-analytic Gaussian Network Aggregation

Meta-analytic Gaussian Network Aggregation

Exploratory and Confirmatory Bayesian Networks Identify the Central Role of Non-judging in Symptoms of Depression

Meta-analytic Gaussian Network Aggregation

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