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      A Cross‐Cultural Comparison of ICD‐11 Complex Posttraumatic Stress Disorder Symptom Networks in Austria, the United Kingdom, and Lithuania

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          Abstract

          The 11th revision of the World Health Organization's International Classification of Diseases ( ICD‐11) includes a new disorder, complex posttraumatic stress disorder (CPTSD). The network approach to psychopathology enables investigation of the structure of disorders at the symptom level, which allows for analysis of direct symptom interactions. The network structure of ICD‐11 CPTSD has not yet been studied, and it remains unclear whether similar networks replicate across different samples. We investigated the network models of four different trauma samples that included a total of 879 participants ( M age = 47.17 years, SD = 11.92; 59.04% women) drawn from Austria, Lithuania, and Scotland and Wales in the United Kingdom. The International Trauma Questionnaire was used to assess symptoms of ICD‐11 CPTSD in all samples. The prevalence of PTSD and CPTSD ranged from 23.7% to 37.3% and from 9.3% to 53.1%, respectively. Regularized partial correlation networks were estimated and the resulting networks compared. Despite several differences in the symptom presentation and cultural background, the networks across the four samples were considerably similar, with high correlations between symptom profiles (ρs = .48–.87), network structures (ρs = .69–.75), and centrality estimates (ρs = .59–.82). These results support the replicability of CPTSD network models across different samples and provide further evidence about the robust structure of CPTSD. The most central symptom in all four sample‐specific networks and the overall network was “feelings of worthlessness.” Implications of the network approach in research and practice are discussed.

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          Sparse inverse covariance estimation with the graphical lasso.

          We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
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            A new method for constructing networks from binary data

            Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.
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              The joint graphical lasso for inverse covariance estimation across multiple classes

              We consider the problem of estimating multiple related Gaussian graphical models from a high-dimensional data set with observations belonging to distinct classes. We propose the joint graphical lasso, which borrows strength across the classes in order to estimate multiple graphical models that share certain characteristics, such as the locations or weights of nonzero edges. Our approach is based upon maximizing a penalized log likelihood. We employ generalized fused lasso or group lasso penalties, and implement a fast ADMM algorithm to solve the corresponding convex optimization problems. The performance of the proposed method is illustrated through simulated and real data examples.
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                Author and article information

                Contributors
                Matthias.knefel@univie.ac.at
                Journal
                J Trauma Stress
                J Trauma Stress
                10.1002/(ISSN)1573-6598
                JTS
                Journal of Traumatic Stress
                John Wiley and Sons Inc. (Hoboken )
                0894-9867
                1573-6598
                28 January 2019
                February 2020
                : 33
                : 1 , NETWORK ANALYSIS OF TRAUMATIC STRESS ( doiID: 10.1002/jts.v33.1 )
                : 41-51
                Affiliations
                [ 1 ] Faculty of Psychology University of Vienna Vienna Austria
                [ 2 ] School of Medicine Cardiff University Cardiff UK
                [ 3 ] School of Health & Social Care Edinburgh Napier University Edinburgh UK
                [ 4 ] Rivers Centre for Traumatic Stress NHS Lothian Edinburgh Edinburgh UK
                [ 5 ] Department of Clinical and Organizational Psychology Vilnius University Vilnius Lithuania
                [ 6 ] Cardiff & Vale University Health Board Cardiff UK
                Author notes
                [*] [* ]Correspondence concerning this article should be addressed to Matthias Knefel, Department of Applied Psychology: Health, Development, Enhancement and Intervention, Faculty of Psychology, University of Vienna, Liebiggasse 5, 1080 Vienna, Austria. Email: Matthias.knefel@ 123456univie.ac.at
                Author information
                https://orcid.org/0000-0003-0632-0673
                https://orcid.org/0000-0002-6654-6220
                Article
                JTS22361
                10.1002/jts.22361
                7155025
                30688371
                707b54aa-410e-43a5-8b62-00bd32ae59d0
                © 2019 The Authors. International Society for Traumatic Stress Studies published by Wiley Periodicals, Inc. on behalf of Society for International Society for Traumatic Stress Studies

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 05 February 2018
                : 24 April 2018
                : 02 May 2018
                Page count
                Figures: 3, Tables: 2, Pages: 11, Words: 8080
                Funding
                Funded by: National Institute of Social Care and Health Research Academic Health Science Collaboration
                Funded by: Austrian Science Fund , open-funder-registry 10.13039/501100002428;
                Award ID: P 26584
                Funded by: Lietuvos Mokslo Taryba , open-funder-registry 10.13039/501100004504;
                Award ID: MIP‐006/2015
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                February 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.0 mode:remove_FC converted:14.04.2020

                Emergency medicine & Trauma
                Emergency medicine & Trauma

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