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Ebrahimi OV, Borsboom D, Hoekstra RHA, Epskamp S, Ostinelli EG, Bastiaansen JA, Cipriani A. Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder. Br J Psychiatry 2024; 224:157-163. [PMID: 38584324 PMCID: PMC11039556 DOI: 10.1192/bjp.2024.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 12/14/2023] [Accepted: 01/16/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics. AIMS To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity. METHOD Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results. RESULTS Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%. CONCLUSIONS The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.
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Affiliation(s)
- Omid V. Ebrahimi
- Department of Experimental Psychology, University of Oxford, Oxford, UK; and Department of Psychology , University of Oslo, Oslo, Norway
| | - Denny Borsboom
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Ria H. A. Hoekstra
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Sacha Epskamp
- Department of Psychology, National University of Singapore, Singapore, Singapore
| | - Edoardo G. Ostinelli
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Precision Psychiatry Laboratory, NIHR Oxford Health Biomedical Research Centre, Oxford, UK; and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Jojanneke A. Bastiaansen
- Interdisciplinary Center Psychopathology and Emotion Regulation, Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; and Friesland Mental Health Care Services, Leeuwarden, The Netherlands
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Precision Psychiatry Laboratory, NIHR Oxford Health Biomedical Research Centre, Oxford, UK; and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
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2
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Hoekstra RHA, Epskamp S, Nierenberg AA, Borsboom D, McNally RJ. Testing similarity in longitudinal networks: The Individual Network Invariance Test. Psychol Methods 2024:2024-71770-001. [PMID: 38602781 DOI: 10.1037/met0000638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
The comparison of idiographic network structures to determine the presence of heterogeneity is a challenging endeavor in many applied settings. Previously, researchers eyeballed idiographic networks, computed correlations, and used techniques that make use of the multilevel structure of the data (e.g., group iterative multiple model estimation and multilevel vector autoregressive) to investigate individual differences. However, these methods do not allow for testing the (in)equality of idiographic network structures directly. In this article, we propose the Individual Network Invariance Test (INIT), which we implemented in the R package INIT. INIT extends common model comparison practices in structural equation modeling to idiographic network structures to test for (in)equality between idiographic networks. In a simulation study, we evaluated the performance of INIT on both saturated and pruned idiographic network structures by inspecting the rejection rate of the χ² difference test and model selection criteria, such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Results show INIT performs adequately when t = 100 per individual. When applying INIT on saturated networks, the AIC performed best as a model selection criterion, while the BIC showed better results when applying INIT on pruned networks. In an empirical example, we highlight the possibilities of this new technique, illustrating how INIT provides researchers with a means of testing for (in)equality between idiographic network structures and within idiographic network structures over time. To conclude, recommendations for empirical researchers are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
| | - Sacha Epskamp
- Department of Psychology, National University of Singapore
| | - Andrew A Nierenberg
- Dauten Family Center for Bipolar Treatment Innovation, Massachusetts General Hospital, Harvard Medical School
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3
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Veenman M, Janssen LHC, van Houtum LAEM, Wever MCM, Verkuil B, Epskamp S, Fried EI, Elzinga BM. A Network Study of Family Affect Systems in Daily Life. Multivariate Behav Res 2024; 59:371-405. [PMID: 38356299 DOI: 10.1080/00273171.2023.2283632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
Adolescence is a time period characterized by extremes in affect and increasing prevalence of mental health problems. Prior studies have illustrated how affect states of adolescents are related to interactions with parents. However, it remains unclear how affect states among family triads, that is adolescents and their parents, are related in daily life. This study investigated affect state dynamics (happy, sad, relaxed, and irritated) of 60 family triads, including 60 adolescents (Mage = 15.92, 63.3% females), fathers and mothers (Mage = 49.16). The families participated in the RE-PAIR study, where they reported their affect states in four ecological momentary assessments per day for 14 days. First, we used multilevel vector-autoregressive network models to estimate affect dynamics across all families, and for each family individually. Resulting models elucidated how family affect states were related at the same moment, and over time. We identified relations from parents to adolescents and vice versa, while considering family variation in these relations. Second, we evaluated the statistical performance of the network model via a simulation study, varying the percentage missing data, the number of families, and the number of time points. We conclude with substantive and statistical recommendations for future research on family affect dynamics.
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Affiliation(s)
- Myrthe Veenman
- Department of Clinical Psychology, Faculty of Social Sciences, Leiden University
| | - Loes H C Janssen
- Department of Clinical Psychology, Faculty of Social Sciences, Leiden University
| | | | - Mirjam C M Wever
- Department of Clinical Psychology, Faculty of Social Sciences, Leiden University
| | - Bart Verkuil
- Department of Clinical Psychology, Faculty of Social Sciences, Leiden University
| | - Sacha Epskamp
- Department of Psychology, National University of Singapore
| | - Eiko I Fried
- Department of Clinical Psychology, Faculty of Social Sciences, Leiden University
| | - Bernet M Elzinga
- Department of Clinical Psychology, Faculty of Social Sciences, Leiden University
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4
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van den Ende MWJ, van der Maas HLJ, Epskamp S, Lees MH. Alcohol consumption as a socially contagious phenomenon in the Framingham Heart Study social network. Sci Rep 2024; 14:4499. [PMID: 38402289 PMCID: PMC11052543 DOI: 10.1038/s41598-024-54155-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 02/09/2024] [Indexed: 02/26/2024] Open
Abstract
We use longitudinal social network data from the Framingham Heart Study to examine the extent to which alcohol consumption is influenced by the network structure. We assess the spread of alcohol use in a three-state SIS-type model, classifying individuals as abstainers, moderate drinkers, and heavy drinkers. We find that the use of three-states improves on the more canonical two-state classification, as the data show that all three states are highly stable and have different social dynamics. We show that when modelling the spread of alcohol use, it is important to model the topology of social interactions by incorporating the network structure. The population is not homogeneously mixed, and clustering is high with abstainers and heavy drinkers. We find that both abstainers and heavy drinkers have a strong influence on their social environment; for every heavy drinker and abstainer connection, the probability of a moderate drinker adopting their drinking behaviour increases by [Formula: see text] and [Formula: see text], respectively. We also find that abstinent connections have a significant positive effect on heavy drinkers quitting drinking. Using simulations, we find that while both are effective, increasing the influence of abstainers appears to be the more effective intervention compared to reducing the influence of heavy drinkers.
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Affiliation(s)
- Maarten W J van den Ende
- Psychological Methods, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands.
- Institute of Advanced Studies, University of Amsterdam, Amsterdam, 1012 GC, The Netherlands.
| | - Han L J van der Maas
- Psychological Methods, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Sacha Epskamp
- Psychological Methods, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
- Department of Psychology, National University of Singapore, Singapore, 117570, Singapore
| | - Mike H Lees
- Institute of Advanced Studies, University of Amsterdam, Amsterdam, 1012 GC, The Netherlands
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5
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Crielaard L, Uleman JF, Châtel BDL, Epskamp S, Sloot PMA, Quax R. Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling. Psychol Methods 2024; 29:169-201. [PMID: 35549316 DOI: 10.1037/met0000484] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Complexity science and systems thinking are increasingly recognized as relevant paradigms for studying systems where biology, psychology, and socioenvironmental factors interact. The application of systems thinking, however, often stops at developing a conceptual model that visualizes the mapping of causal links within a system, e.g., a causal loop diagram (CLD). While this is an important contribution in itself, it is imperative to subsequently formulate a computable version of a CLD in order to interpret the dynamics of the modeled system and simulate "what if" scenarios. We propose to realize this by deriving knowledge from experts' mental models in biopsychosocial domains. This article first describes the steps required for capturing expert knowledge in a CLD such that it may result in a computational system dynamics model (SDM). For this purpose, we introduce several annotations to the CLD that facilitate this intended conversion. This annotated CLD (aCLD) includes sources of evidence, intermediary variables, functional forms of causal links, and the distinction between uncertain and known-to-be-absent causal links. We propose an algorithm for developing an aCLD that includes these annotations. We then describe how to formulate an SDM based on the aCLD. The described steps for this conversion help identify, quantify, and potentially reduce sources of uncertainty and obtain confidence in the results of the SDM's simulations. We utilize a running example that illustrates each step of this conversion process. The systematic approach described in this article facilitates and advances the application of computational science methods to biopsychosocial systems. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
| | | | | | - Sacha Epskamp
- Psychological Methods Group, Department of Psychology, University of Amsterdam
| | | | - Rick Quax
- Institute for Advanced Study, University of Amsterdam
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6
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Freichel R, Skjerdingstad N, Mansueto AC, Epskamp S, Hoffart A, Johnson SU, Ebrahimi OV. Use of substances to cope predicts posttraumatic stress disorder symptom persistence: Investigating patterns of interactions between symptoms and its maintaining mechanisms. Psychol Trauma 2023:2024-33774-001. [PMID: 38059942 DOI: 10.1037/tra0001624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
OBJECTIVE Posttraumatic stress disorder (PTSD) remains a growing public health challenge across the globe and is associated with negative and persistent long-term consequences. The last decades of research have identified different mechanisms associated with the development and persistence of PTSD, including maladaptive coping strategies, cognitive and experiential avoidance, and positive and negative metacognitions. Despite these advances, little is known about how these different processes interact with specific PTSD symptoms, and how they influence each other over time at the within-person level. METHOD Leveraging a large (N > 1,800) longitudinal data set representative of the Norwegian population during the COVID-19 pandemic, this preregistered study investigated these symptom-process interactions over four assessment waves spanning an 8-month period. RESULTS Our panel graphical vector autoregressive network model revealed the dominating role of substance use to cope in predicting higher levels of PTSD symptoms over time and increases in PTSD symptomatology within more proximal time windows (i.e., within 6 weeks). Threat monitoring was associated with increased suicidal ideation, while threat monitoring itself was increasing upon decreased avoidance behavior, greater presence of negative metacognitions, and higher use of substances to cope. CONCLUSIONS Our findings speak to the importance of attending to different coping strategies, particularly the use of substances as a coping behavior in efforts to prevent PTSD chronicity upon symptom onset. We outline future directions for research efforts to better understand the complex interactions and temporal pathways leading up to the development and maintenance of PTSD symptomatology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
| | | | | | - Sacha Epskamp
- Department of Psychology, National University of Singapore
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7
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Abstract
Recent times have seen a call for personalized psychotherapy and tailored communication during treatment, leading to the necessity to model the complex dynamics of mental disorders in a single subject. To this aim, time-series data in one patient can be collected through ecological momentary assessment and analyzed with the graphical vector autoregressive model, estimating temporal and contemporaneous idiographic networks. Idiographic networks graph interindividual processes that may be potentially used to tailor psychotherapy and provide personalized feedback to clients and are regarded as a promising tool for clinical practice. However, the question whether we can reliably estimate them in clinical settings remains unanswered. We conducted a large-scale simulation study in the context of psychopathology, testing the performance of personalized networks with different numbers of time points, percentages of missing data, and estimation methods. Results indicate that sensitivity is low with sample sizes feasible for clinical practice (75 and 100 time points). It seems possible to retrieve the global network structure but not to recover the full network. Estimating temporal networks appears particularly challenging; thus, with 75 and 100 observations, it is advisable to reduce the number of nodes to around six variables. With regard to missing data, full information maximum likelihood and the Kalman filter are effective in addressing random item-level missing data; consequently, planned missingness is a valid method to deal with missing data. We discuss possible methodological and clinical solutions to the challenges raised in this work. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
| | | | | | | | - Sacha Epskamp
- Centre for Urban Mental Health, University of Amsterdam
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8
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Abstract
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 data sets. 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 data sets. 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 have highlighted, alongside desirable asymptotic properties and low sample size discovery, results according to most common research questions in the field. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
| | - Sacha Epskamp
- Department of Psychology, Psychological Methods, University of Amsterdam
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9
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Burger J, Isvoranu AM, Lunansky G, Haslbeck JMB, Epskamp S, Hoekstra RHA, Fried EI, Borsboom D, Blanken TF. Reporting standards for psychological network analyses in cross-sectional data. Psychol Methods 2023; 28:806-824. [PMID: 35404629 DOI: 10.1037/met0000471] [Citation(s) in RCA: 67] [Impact Index Per Article: 67.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
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. So far, researchers have 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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Julian Burger
- Amsterdam Centre for Urban Mental Health, University of Amsterdam
| | | | | | | | - Sacha Epskamp
- Amsterdam Centre for Urban Mental Health, University of Amsterdam
| | | | - Eiko I Fried
- Department of Clinical Psychology, Leiden University
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10
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Abstract
The use of idiographic research techniques has gained popularity within psychological research and network analysis in particular. Idiographic research has been proposed as a promising avenue for future research, with differences between idiographic results highlighting evidence for radical heterogeneity. However, in the quest to address the individual in psychology, some classic statistical problems, such as those arising from sampling variation and power limitations, should not be overlooked. This article aims to determine to what extent current tools to compare idiographic networks are suited to disentangle true from illusory heterogeneity in the presence of sampling error. To this end, we investigate the performance of tools to inspect heterogeneity (visual inspection, comparison of centrality measures, investigating standard deviations of random effects, and GIMME) through simulations. Results show that power limitations hamper the validity of conclusions regarding heterogeneity and that the power required to assess heterogeneity adequately is often not realized in current research practice. Of the tools investigated, inspecting standard deviations of random effects and GIMME proved the most suited. However, all tools evaluated leave the door wide open to misinterpret all observed variability in terms of individual differences. Hence, the current paper calls for caution in the use and interpretation of new time-series techniques when it comes to heterogeneity.
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Affiliation(s)
| | - Sacha Epskamp
- Department of Psychology, University of Amsterdam
- Amsterdam Centre for Urban Mental Health
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11
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Jongerling J, Epskamp S, Williams DR. Bayesian Uncertainty Estimation for Gaussian Graphical Models and Centrality Indices. Multivariate Behav Res 2023; 58:311-339. [PMID: 35180031 DOI: 10.1080/00273171.2021.1978054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
In the network approach to psychopathology, psychological constructs are conceptualized as networks of interacting components (e.g., the symptoms of a disorder). In this network view, interest is on the degree to which symptoms influence each other, both directly and indirectly. These direct and indirect influences are often captured with centrality indices, however, the estimation method often used with these networks, the frequentist graphical LASSO (GLASSO), has difficulty estimating (uncertainty in) these measures. Bayesian estimation might provide a solution, as it is better suited to deal with bias in the sampling distribution of centrality indices. This study therefore compares estimation of symptom networks with Bayesian GLASSO- and Horseshoe priors to estimation using the frequentist GLASSO using extensive simulations. Results showed that the Bayesian GLASSO performed better than the Horseshoe, and that the Bayesian GLASSO outperformed the frequentist GLASSO with respect to bias in edge weights, centrality measures, correlation between estimated and true partial correlations, and specificity. Sensitivity was better for the frequentist GLASSO, but performance of the Bayesian GLASSO is usually close. With respect to uncertainty in the centrality measures, the Bayesian GLASSO shows good coverage for strength and closeness centrality, but uncertainty in betweenness centrality is estimated less well.
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Affiliation(s)
- Joran Jongerling
- Department of Methodology and Statistics, Tilburg School of Social and Behavioral Sciences, Tilburg University
| | - Sacha Epskamp
- Department of Psychology, Faculty of Social and Behavioral Sciences, University of Amsterdam
- Centre for Urban Mental Health, University of Amsterdam
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12
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Burger J, Epskamp S, van der Veen DC, Dablander F, Schoevers RA, Fried EI, Riese H. A clinical PREMISE for personalized models: Toward a formal integration of case formulations and statistical networks. J Psychopathol Clin Sci 2022; 131:906-916. [PMID: 36326631 DOI: 10.1037/abn0000779] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Over the past decade, the idiographic approach has received significant attention in clinical psychology, incentivizing the development of novel approaches to estimate statistical models, such as personalized networks. Although the notion of such networks aligns well with the way clinicians think and reason, there are currently several barriers to implementation that limit their clinical utility. To address these issues, we introduce the Prior Elicitation Module for Idiographic System Estimation (PREMISE), a novel approach that formally integrates case formulations with personalized network estimation via prior elicitation and Bayesian inference. PREMISE tackles current implementation barriers of personalized networks; incorporating clinical information into personalized network estimation systematically allows theoretical and data-driven integration, supporting clinician and patient collaboration when building a dynamic understanding of the patient's psychopathology. To illustrate its potential, we estimate clinically informed networks for a patient suffering from obsessive-compulsive disorder. We discuss open challenges in selecting statistical models for PREMISE, as well as specific future directions for clinical implementation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Affiliation(s)
- Julian Burger
- University Center Psychiatry (UCP) Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen
| | - Sacha Epskamp
- Department of Psychology, National University of Singapore
| | - Date C van der Veen
- University Center Psychiatry (UCP) Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen
| | | | - Robert A Schoevers
- University Center Psychiatry (UCP) Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen
| | - Eiko I Fried
- Department of Clinical Psychology, Leiden University
| | - Harriëtte Riese
- University Center Psychiatry (UCP) Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen
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13
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Affiliation(s)
- Sacha Epskamp
- Department of Psychology and Centre for Urban Mental HealthUniversity of AmsterdamAmsterdamThe Netherlands
| | - Adela‐Maria Isvoranu
- Department of Psychology and Centre for Urban Mental HealthUniversity of AmsterdamAmsterdamThe Netherlands
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14
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de Ron J, Robinaugh DJ, Fried EI, Pedrelli P, Jain FA, Mischoulon D, Epskamp S. Quantifying and addressing the impact of measurement error in network models. Behav Res Ther 2022; 157:104163. [PMID: 36030733 PMCID: PMC10786122 DOI: 10.1016/j.brat.2022.104163] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 06/30/2022] [Accepted: 07/12/2022] [Indexed: 11/02/2022]
Abstract
Network psychometric models are often estimated using a single indicator for each node in the network, thus failing to consider potential measurement error. In this study, we investigate the impact of measurement error on cross-sectional network models. First, we conduct a simulation study to evaluate the performance of models based on single indicators as well as models that utilize information from multiple indicators per node, including average scores, factor scores, and latent variables. Our results demonstrate that measurement error impairs the reliability and performance of network models, especially when using single indicators. The reliability and performance of network models improves substantially with increasing sample size and when using methods that combine information from multiple indicators per node. Second, we use empirical data from the STAR*D trial (n = 3,731) to further evaluate the impact of measurement error. In the STAR*D trial, depression symptoms were assessed via three questionnaires, providing multiple indicators per symptom. Consistent with our simulation results, we find that when using sub-samples of this dataset, the discrepancy between the three single-indicator networks (one network per questionnaire) diminishes with increasing sample size. Together, our simulated and empirical findings provide evidence that measurement error can hinder network estimation when working with smaller samples and offers guidance on methods to mitigate measurement error.
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Affiliation(s)
- Jill de Ron
- Department of Psychological Methods, University of Amsterdam, the Netherlands.
| | - Donald J Robinaugh
- Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, USA; Department of Applied Psychology, Northeastern University, USA
| | - Eiko I Fried
- Department of Clinical Psychology, Leiden University, the Netherlands
| | - Paola Pedrelli
- Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, USA
| | - Felipe A Jain
- Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, USA
| | - David Mischoulon
- Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, USA
| | - Sacha Epskamp
- Department of Psychological Methods, University of Amsterdam, the Netherlands; Centre for Urban Mental Health, University of Amsterdam, the Netherlands
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15
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Epskamp S, van der Maas HLJ, Peterson RE, van Loo HM, Aggen SH, Kendler KS. Intermediate stable states in substance use. Addict Behav 2022; 129:107252. [PMID: 35182945 DOI: 10.1016/j.addbeh.2022.107252] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 11/15/2022]
Abstract
Many people across the world use potentially addictive legal and illegal substances, but evidence suggests that not all use leads to heavy use and dependence, as some substances are used moderately for long periods of time. Here, we empirically examine, the stability of and transitions between three substance use states: zero-use, moderate use, and heavy use. We investigate two large datasets from the US and the Netherlands on yearly usage and change of alcohol, nicotine, and cannabis. Results, which we make available through an extensive interactive tool, suggests that there are stable moderate use states, even after meeting criteria for a positive diagnosis of substance abuse or dependency, for both alcohol and cannabis use. Moderate use of tobacco, however, was rare. We discuss implications of recognizing three states rather than two states as a modeling target, in which the moderate use state can both act as an intervention target or as a gateway between zero use and heavy use.
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Affiliation(s)
- Sacha Epskamp
- University of Amsterdam, Department of Psychology, Psychological Methods Program Group, Amsterdam, The Netherlands; University of Amsterdam, Centre for Urban Mental Health, Amsterdam, The Netherlands.
| | - Han L J van der Maas
- University of Amsterdam, Department of Psychology, Psychological Methods Program Group, Amsterdam, The Netherlands
| | - Roseann E Peterson
- Virginia Commonwealth University, Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Hanna M van Loo
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, The Netherlands
| | - Steven H Aggen
- Virginia Commonwealth University, Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Kenneth S Kendler
- Virginia Commonwealth University, Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
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16
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van den Ende MW, Epskamp S, Lees MH, van der Maas HL, Wiers RW, Sloot PM. A review of mathematical modeling of addiction regarding both (neuro-) psychological processes and the social contagion perspectives. Addict Behav 2022; 127:107201. [PMID: 34959078 DOI: 10.1016/j.addbeh.2021.107201] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 07/04/2021] [Accepted: 11/22/2021] [Indexed: 12/16/2022]
Abstract
Addiction is a complex biopsychosocial phenomenon, impacted by biological predispositions, psychological processes, and the social environment. Using mathematical and computational models that allow for surrogative reasoning may be a promising avenue for gaining a deeper understanding of this complex behavior. This paper reviews and classifies a selection of formal models of addiction focusing on the intra- and inter-individual dynamics, i.e., (neuro) psychological models and social models. We find that these modeling approaches to addiction are too disjoint and argue that in order to unravel the complexities of biopsychosocial processes of addiction, models should integrate intra- and inter-individual factors.
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17
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Epskamp S, Isvoranu AM, Cheung MWL. Erratum to: Meta-analytic Gaussian Network Aggregation. Psychometrika 2022; 87:372. [PMID: 35089497 PMCID: PMC9172819 DOI: 10.1007/s11336-021-09804-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Affiliation(s)
- Sacha Epskamp
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands.
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands.
| | | | - Mike W-L Cheung
- Department of Psychology, National University of Singapore, Singapore, Singapore
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18
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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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Affiliation(s)
- Sacha Epskamp
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands.
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands.
| | | | - Mike W-L Cheung
- Department of Psychology, National University of Singapore, Singapore, Singapore
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19
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Koelen JA, Mansueto AC, Finnemann A, de Koning L, van der Heijde CM, Vonk P, Wolters NE, Klein A, Epskamp S, Wiers RW. COVID-19 and mental health among at-risk university students: A prospective study into risk and protective factors. Int J Methods Psychiatr Res 2022; 31:e1901. [PMID: 34932250 PMCID: PMC8886289 DOI: 10.1002/mpr.1901] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 10/29/2021] [Accepted: 12/07/2021] [Indexed: 01/11/2023] Open
Abstract
OBJECTIVE The COVID-19 pandemic has confronted young adults with an unprecedented mental health challenge. Yet, prospective studies examining protective factors are limited. METHODS In the present study, we focused on changes in mental health in a large sample (N = 685) of at-risk university students, which were measured before and during the pandemic. Network modeling was applied to 20 measured variables to explore intercorrelations between mental health factors, and to identify risk and protective factors. Latent change score modeling was used on a subset of variables. RESULTS The main findings indicate that (1) mental health problems increased at group level, especially depression-anxiety and loneliness; (2) emotional support during the COVID pandemic was associated with smaller increases in loneliness and depression-anxiety; (3) COVID-related stress predicted increases in depression-anxiety; (4) loneliness acted as a bridge construct between emotional support and changes in mental health. CONCLUSION To mitigate the impact of the COVID-19 pandemic on the mental health of young adults, is it recommended to focus on interventions that strengthen internal resources (stress-regulating abilities) and reduce loneliness.
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Affiliation(s)
- Jurrijn A Koelen
- Developmental Psychology, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Alessandra C Mansueto
- Developmental Psychology, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.,Center for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands.,Psychological Methods, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.,Amsterdam School of Communication Research (ASCoR), University of Amsterdam, Amsterdam, The Netherlands
| | - Adam Finnemann
- Center for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands.,Psychological Methods, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Lisa de Koning
- Developmental Psychology, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Claudia M van der Heijde
- Department of Research, Development and Prevention, Student Health Service, University of Amsterdam, Amsterdam, The Netherlands
| | - Peter Vonk
- Department of Research, Development and Prevention, Student Health Service, University of Amsterdam, Amsterdam, The Netherlands
| | - Nine E Wolters
- Developmental Psychology, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Anke Klein
- Developmental Psychology, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Sacha Epskamp
- Center for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands.,Psychological Methods, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Reinout W Wiers
- Developmental Psychology, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.,Center for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands.,Department of Psychology, Addiction Development and Psychopathology (ADAPT)-Lab, University of Amsterdam, Amsterdam, The Netherlands
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20
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Bringmann LF, Albers C, Bockting C, Borsboom D, Ceulemans E, Cramer A, Epskamp S, Eronen MI, Hamaker E, Kuppens P, Lutz W, McNally RJ, Molenaar P, Tio P, Voelkle MC, Wichers M. Psychopathological networks: Theory, methods and practice. Behav Res Ther 2021; 149:104011. [PMID: 34998034 DOI: 10.1016/j.brat.2021.104011] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 11/05/2021] [Accepted: 11/27/2021] [Indexed: 12/19/2022]
Abstract
In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.
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Affiliation(s)
- Laura F Bringmann
- University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), P.O. Box 30.001 (CC72), 9700 RB, Groningen, the Netherlands; University of Groningen, Faculty of Behavioural and Social Sciences, Department of Psychometrics and Statistics, Grote Kruisstraat 2/1, 9712 TS, Groningen, the Netherlands.
| | - Casper Albers
- University of Groningen, Faculty of Behavioural and Social Sciences, Department of Psychometrics and Statistics, Grote Kruisstraat 2/1, 9712 TS, Groningen, the Netherlands
| | - Claudi Bockting
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam UMC, Location AMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Denny Borsboom
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
| | - Eva Ceulemans
- KU Leuven, Faculty of Psychology and Educational Sciences, Leuven, Belgium
| | - Angélique Cramer
- RIVM National Institute for Public Health and the Environment, the Netherlands
| | - Sacha Epskamp
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, the Netherlands; Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
| | - Markus I Eronen
- Department of Theoretical Philosophy, University of Groningen, the Netherlands
| | - Ellen Hamaker
- Department of Methodology and Statistics, Utrecht University, the Netherlands
| | - Peter Kuppens
- KU Leuven, Faculty of Psychology and Educational Sciences, Leuven, Belgium
| | - Wolfgang Lutz
- Department of Psychology, University of Trier, Germany
| | | | - Peter Molenaar
- Department of Human Development and Family Studies, The Pennsylvania State University, USA
| | - Pia Tio
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Department of Methodology and Statistics, Tilburg University, Tilburg, the Netherlands
| | - Manuel C Voelkle
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Marieke Wichers
- University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), P.O. Box 30.001 (CC72), 9700 RB, Groningen, the Netherlands
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21
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Abstract
Posttraumatic 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 in the first-ever meta-analytic study of PTSD symptom networks. 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 data sets, investigated the scope of between-study heterogeneity, and assessed the performance of network models estimated from single studies. Our main findings are that: (a) 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. (b) While several clear symptom-links, interpretable clusters, and significant differences between strength of edges and centrality of nodes can be identified in the network, no single or small set of nodes that clearly played a more central role than other nodes could be pinpointed, except for the symptom "amnesia" that was clearly the least central symptom. (c) Despite large between-study heterogeneity, we found that network models estimated from single samples can 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. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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22
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Isvoranu AM, Epskamp S. Which estimation method to choose in network psychometrics? Deriving guidelines for applied researchers. Psychol Methods 2021:2022-06515-001. [PMID: 34843277 DOI: 10.31234/osf.io/mbycn] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
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 data sets. 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 data sets. 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 have highlighted, alongside desirable asymptotic properties and low sample size discovery, results according to most common research questions in the field. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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23
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Liu D, Epskamp S, Isvoranu AM, Chen C, Liu W, Hong X. Network analysis of physical and psychiatric symptoms of hospital discharged patients infected with COVID-19. J Affect Disord 2021; 294:707-713. [PMID: 34343929 PMCID: PMC8284061 DOI: 10.1016/j.jad.2021.07.043] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 07/08/2021] [Accepted: 07/13/2021] [Indexed: 12/01/2022]
Abstract
In the current study, we aimed to investigate the network structure of COVID-19 symptoms and its related psychiatric symptoms, using a network approach. Specifically, we examined how COVID-19 symptoms relate to psychiatric symptoms and highlighted potential pathways between COVID-19 severity and psychiatric symptoms. With a sample of six hundred seventy-five recovered COVID-19 patients recruited 1 month after hospital discharge, we respectively integrated COVID-19 symptoms with PTSD, depression, and anxiety symptoms and analyzed the three network structures. In all three networks, COVID-19 severity and ICU admission are not linked directly to COVID-19 symptoms after hospitalization, while COVID-19 severity (but not ICU admission) is linked directly to one or more psychiatric symptoms. Specific pathways between COVID-19 symptoms and psychiatric symptoms were discussed. Finally, we used directed acyclic graph estimation to show potential causal effects between COVID-19 related variables and demographic characteristics.
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Affiliation(s)
- Dong Liu
- Department of Communication, Renmin University of China, China.
| | - Sacha Epskamp
- Department of Psychology, Psychological Methods Group, University of Amsterdam, the Netherlands; Centre for Urban Mental Health, Amsterdam, the Netherlands.
| | - Adela-Maria Isvoranu
- Department of Psychology, Psychological Methods Group, University of Amsterdam, the Netherlands
| | - Caixia Chen
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wenjun Liu
- Department of Communication, Renmin University of China, China
| | - Xinyi Hong
- Department of Communication, Renmin University of China, China
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24
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Schumacher L, Burger J, Zoellner F, Zindler A, Epskamp S, Barthel D. Using clinical expertise and empirical data in constructing networks of trauma symptoms in refugee youth. Eur J Psychotraumatol 2021; 12:1920200. [PMID: 34178294 PMCID: PMC8205066 DOI: 10.1080/20008198.2021.1920200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background: In recent years, many adolescents have fled their home countries due to war and human rights violations, consequently experiencing various traumatic events and putting them at risk of developing mental health problems. The symptomatology of refugee youth was shown to be multifaceted and often falling outside of traditional diagnoses. Objective: The present study aimed to investigate the symptomatology of this patient group by assessing the network structure of a wide range of symptoms. Further, we assessed clinicians' perceptions of symptoms relations in order to evaluate the clinical validity of the empirical network. Methods: Empirical data on Post-Traumatic Stress Disorder (PTSD), depression and other trauma symptoms from N = 366 refugee youth were collected during the routine diagnostic process of an outpatient centre for refugee youth in Germany. Additionally, four clinicians of this outpatient centre were asked how they perceive symptom relations in their patients using a newly developed tool. Separate networks were constructed based on 1) empirical symptom data and 2) clinicians' perceived symptom relations (PSR). Results: Both the network based on empirical data and the network based on clinicians' PSR showed that symptoms of PTSD and depression related most strongly within each respective cluster (connected mainly via sleeping problems), externalizing symptoms were somewhat related to PTSD symptoms and intrusions were central. Some differences were found within the clinicians' PSR as well as between the PSR and the empirical network. Still, the general PSR-network structure showed a moderate to good fit to the empirical data. Conclusion: Our results suggest that sleeping problems and intrusions play a central role in the symptomatology of refugee children, which has tentative implications for diagnostics and treatment. Further, externalizing symptoms might be an indicator for PTSD-symptoms. Finally, using clinicians' PSR for network construction offered a promising possibility to gain information on symptom networks and their clinical validity.
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Affiliation(s)
- Lea Schumacher
- Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Department of Psychology, Developmental Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Julian Burger
- Interdisciplinary Center Psychopathology and Emotion Regulation, University Center Psychiatry (UCP), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Center for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands
| | - Fionna Zoellner
- Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Areej Zindler
- Ambulanzzentrum des UKE GmbH, Flüchtlingsambulanz, Hamburg, Germany
| | - Sacha Epskamp
- Center for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands.,Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Dana Barthel
- Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Ambulanzzentrum des UKE GmbH, Flüchtlingsambulanz, Hamburg, Germany
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25
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Abstract
The Ising model is a model for pairwise interactions between binary variables that has become popular in the psychological sciences. It has been first introduced as a theoretical model for the alignment between positive (1) and negative (-1) atom spins. In many psychological applications, however, the Ising model is defined on the domain {0, 1} instead of the classical domain {-1,1}. While it is possible to transform the parameters of the Ising model in one domain to obtain a statistically equivalent model in the other domain, the parameters in the two versions of the Ising model lend themselves to different interpretations and imply different dynamics, when studying the Ising model as a dynamical system. In this tutorial paper, we provide an accessible discussion of the interpretation of threshold and interaction parameters in the two domains and show how the dynamics of the Ising model depends on the choice of domain. Finally, we provide a transformation that allows one to transform the parameters in an Ising model in one domain into a statistically equivalent Ising model in the other domain.
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Affiliation(s)
| | - Sacha Epskamp
- Psychological Methods Group, University of Amsterdam
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26
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Epskamp S, Fried EI, van Borkulo CD, Robinaugh DJ, Marsman M, Dalege J, Rhemtulla M, Cramer AOJ. Investigating the Utility of Fixed-margin Sampling in Network Psychometrics. Multivariate Behav Res 2021; 56:314-328. [PMID: 30463456 DOI: 10.1080/00273171.2018.1489771] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Revised: 06/04/2018] [Accepted: 06/05/2018] [Indexed: 06/09/2023]
Abstract
Steinley, Hoffman, Brusco, and Sher (2017) proposed a new method for evaluating the performance of psychological network models: fixed-margin sampling. The authors investigated LASSO regularized Ising models (eLasso) by generating random datasets with the same margins as the original binary dataset, and concluded that many estimated eLasso parameters are not distinguishable from those that would be expected if the data were generated by chance. We argue that fixed-margin sampling cannot be used for this purpose, as it generates data under a particular null-hypothesis: a unidimensional factor model with interchangeable indicators (i.e., the Rasch model). We show this by discussing relevant psychometric literature and by performing simulation studies. Results indicate that while eLasso correctly estimated network models and estimated almost no edges due to chance, fixed-margin sampling performed poorly in classifying true effects as "interesting" (Steinley et al. 2017, p. 1004). Further simulation studies indicate that fixed-margin sampling offers a powerful method for highlighting local misfit from the Rasch model, but performs only moderately in identifying global departures from the Rasch model. We conclude that fixed-margin sampling is not up to the task of assessing if results from estimated Ising models or other multivariate psychometric models are due to chance.
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Affiliation(s)
- Sacha Epskamp
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Eiko I Fried
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Claudia D van Borkulo
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Donald J Robinaugh
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, Massachusetts General Hospital, Cambridge, MA, USA
| | - Maarten Marsman
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Jonas Dalege
- Department of Social Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Mijke Rhemtulla
- Department of Psychology, University of California, Davis, CA, USA
| | - Angélique O J Cramer
- Social and Behavioral Sciences, Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands
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27
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Fried EI, van Borkulo CD, Epskamp S. On the Importance of Estimating Parameter Uncertainty in Network Psychometrics: A Response to Forbes et al. (2019). Multivariate Behav Res 2021; 56:243-248. [PMID: 32264714 DOI: 10.1080/00273171.2020.1746903] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
In their recent paper, Forbes et al. (2019; FWMK) evaluate the replicability of network models in two studies. They identify considerable replicability issues, concluding that "current 'state-of-the-art' methods in the psychopathology network literature […] are not well-suited to analyzing the structure of the relationships between individual symptoms". Such strong claims require strong evidence, which the authors do not provide. FWMK identify low replicability by analyzing point estimates of networks; contrast low replicability with results of two statistical tests that indicate higher replicability, and conclude that these tests are problematic. We make four points. First, statistical tests are superior to the visual inspection of point estimates, because tests take into account sampling variability. Second, FWMK misinterpret the statistical tests in several important ways. Third, FWMK did not follow established recommendations when estimating networks in their first study, underestimating replicability. Fourth, FWMK draw conclusions about methodology, which does not follow from investigations of data, and requires investigations of methodology. Overall, we show that the "poor replicability "observed by FWMK occurs due to sampling variability and use of suboptimal methods. We conclude by discussing important recent simulation work that guides researchers to use models appropriate for their data, such as nonregularized estimation routines.
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Affiliation(s)
- Eiko I Fried
- Department of Clinical Psychology, Leiden University, Leiden, Netherlands
| | - Claudia D van Borkulo
- Psychological Methods, Universiteit van Amsterdam Faculteit der Maatschappij- en Gedragswetenschappen, Amsterdam, Netherlands
| | - Sacha Epskamp
- Department of Psychological Methods, Universiteit van Amsterdam, Amsterdam, Netherlands
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28
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Abstract
BACKGROUND In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson's bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson's bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data. METHODS In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants. RESULTS The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson's bias literature, selection reduced recovery rates by inducing negative connections between the items. CONCLUSION Our findings provide evidence that Berkson's bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson's bias and their pitfalls.
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Affiliation(s)
- Jill de Ron
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Eiko I Fried
- Department of Clinical Psychology, Leiden University, Leiden, The Netherlands
| | - Sacha Epskamp
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
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29
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Levinson CA, Cash E, Welch K, Epskamp S, Hunt RA, Williams BM, Keshishian AC, Spoor SP. Personalized networks of eating disorder symptoms predicting eating disorder outcomes and remission. Int J Eat Disord 2020; 53:2086-2094. [PMID: 33179347 PMCID: PMC7864225 DOI: 10.1002/eat.23398] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 10/14/2020] [Accepted: 10/14/2020] [Indexed: 12/30/2022]
Abstract
Enhanced cognitive-behavioral therapy (CBT-E) is one of the primary evidence-based treatments for adults with eating disorders (EDs). However, up to 50% of individuals do not respond to CBT-E, likely because of the high heterogeneity present even within similar diagnoses. This high heterogeneity, especially in regard to presenting pathology, makes it difficult to develop a treatment based "on averages" and for clinicians to accurately pinpoint which symptoms should be targeted in treatment. As such, new models based at both the group, and individual level, are needed to more accurately refine targets for personalized evidence-based treatments that can lead to full remission. The current study (Expected N = 120 anorexia nervosa, atypical anorexia nervosa, and bulimia nervosa) will build both group and individual longitudinal models of ED behaviors, cognitions, affect, and physiology. We will collect data for 30 days utilizing a mobile application to assess behaviors, cognition, and affect and a sensor wristband that assesses physiology (heart rate, acceleration). We will also collect outcome data at 1- and 6-month follow-ups to assess ED outcomes and remission status. These data will allow for identification of "on average" and "individual" targets that maintain ED pathology and test if these targets predict outcomes, including ED remission.
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Affiliation(s)
- Cheri A. Levinson
- Department of Psychological and Brain Sciences, University of Louisville, Louisville, Kentucky
| | - Elizabeth Cash
- School of Medicine, University of Louisville, Louisville, Kentucky
| | - Karla Welch
- Department of Engineering, University of Louisville, Louisville, Kentucky
| | - Sacha Epskamp
- Department of Psychological Methods and Psychometrics, University of Amsterdam, Amsterdam, The Netherlands
| | - Rowan A. Hunt
- Department of Psychological and Brain Sciences, University of Louisville, Louisville, Kentucky
| | - Brenna M. Williams
- Department of Psychological and Brain Sciences, University of Louisville, Louisville, Kentucky
| | - Ani C. Keshishian
- Department of Psychological and Brain Sciences, University of Louisville, Louisville, Kentucky
| | - Samantha P. Spoor
- Department of Psychological and Brain Sciences, University of Louisville, Louisville, Kentucky
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30
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Kan KJ, de Jonge H, van der Maas HLJ, Levine SZ, Epskamp S. How to Compare Psychometric Factor and Network Models. J Intell 2020; 8:jintelligence8040035. [PMID: 33023229 PMCID: PMC7709577 DOI: 10.3390/jintelligence8040035] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 09/17/2020] [Accepted: 09/24/2020] [Indexed: 12/19/2022] Open
Abstract
In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to write, shortly before his death. We first discuss the statistical procedure McFarland used, which involved structural equation modeling (SEM) in standard SEM software. Next, we evaluate the penta-factor model of intelligence. We conclude that (1) standard SEM software is not suitable for the comparison of psychometric networks with latent variable models, and (2) the penta-factor model of intelligence is only of limited value, as it is nonidentified. We conclude with a reanalysis of the Wechlser Adult Intelligence Scale data McFarland discussed and illustrate how network and latent variable models can be compared using the recently developed R package Psychonetrics. Of substantive theoretical interest, the results support a network interpretation of general intelligence. A novel empirical finding is that networks of intelligence replicate over standardization samples.
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Affiliation(s)
- Kees-Jan Kan
- Research Institute of Child Development and Education, University of Amsterdam, 1018 WS Amsterdam, The Netherlands;
- Correspondence:
| | - Hannelies de Jonge
- Research Institute of Child Development and Education, University of Amsterdam, 1018 WS Amsterdam, The Netherlands;
| | - Han L. J. van der Maas
- Department of Psychology, University of Amsterdam, 1018 WS Amsterdam, The Netherlands; (H.L.J.v.d.M.); (S.E.)
| | - Stephen Z. Levine
- Department of Community Mental Health, University of Haifa, Haifa 3498838, Israel;
| | - Sacha Epskamp
- Department of Psychology, University of Amsterdam, 1018 WS Amsterdam, The Netherlands; (H.L.J.v.d.M.); (S.E.)
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Bastiaansen JA, Kunkels YK, Blaauw FJ, Boker SM, Ceulemans E, Chen M, Chow SM, de Jonge P, Emerencia AC, Epskamp S, Fisher AJ, Hamaker EL, Kuppens P, Lutz W, Meyer MJ, Moulder R, Oravecz Z, Riese H, Rubel J, Ryan O, Servaas MN, Sjobeck G, Snippe E, Trull TJ, Tschacher W, van der Veen DC, Wichers M, Wood PK, Woods WC, Wright AGC, Albers CJ, Bringmann LF. Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology. J Psychosom Res 2020; 137:110211. [PMID: 32862062 PMCID: PMC8287646 DOI: 10.1016/j.jpsychores.2020.110211] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 07/15/2020] [Accepted: 07/31/2020] [Indexed: 11/28/2022]
Abstract
OBJECTIVE One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual's emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them. METHODS To evaluate this, we crowdsourced the analysis of one individual patient's ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. RESULTS Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0-16) and nature of selected targets varied widely. CONCLUSION This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation.
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Affiliation(s)
- Jojanneke A Bastiaansen
- Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands; Department of Education and Research, Friesland Mental Health Care Services, Leeuwarden, the Netherlands
| | - Yoram K Kunkels
- Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
| | - Frank J Blaauw
- Department of Psychology, University of Groningen, Groningen, the Netherlands; Distributed Systems group, Faculty of Science and Engineering, University of Groningen, Groningen, the Netherlands
| | - Steven M Boker
- Department of Psychology, University of Virginia, Charlottesville, USA
| | - Eva Ceulemans
- Faculty of Psychology and Educational Sciences, University of Leuven, Leuven, Belgium
| | - Meng Chen
- Department of Human Development and Family Studies, Pennsylvania State University, State College, USA
| | - Sy-Miin Chow
- Department of Human Development and Family Studies, Pennsylvania State University, State College, USA
| | - Peter de Jonge
- Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands; Department of Psychology, University of Groningen, Groningen, the Netherlands
| | - Ando C Emerencia
- Department of Psychology, University of Groningen, Groningen, the Netherlands
| | - Sacha Epskamp
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Aaron J Fisher
- Department of Psychology, University of California Berkeley, Berkeley, USA
| | - Ellen L Hamaker
- Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, the Netherlands
| | - Peter Kuppens
- Faculty of Psychology and Educational Sciences, University of Leuven, Leuven, Belgium
| | - Wolfgang Lutz
- Department of Psychology, University of Trier, Trier, Germany
| | - M Joseph Meyer
- Department of Psychology, University of Virginia, Charlottesville, USA
| | - Robert Moulder
- Department of Psychology, University of Virginia, Charlottesville, USA
| | - Zita Oravecz
- Department of Human Development and Family Studies, Pennsylvania State University, State College, USA
| | - Harriëtte Riese
- Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
| | - Julian Rubel
- Department of Psychology, Justus-Liebig-University Giessen, Germany
| | - Oisín Ryan
- Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, the Netherlands
| | - Michelle N Servaas
- Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
| | - Gustav Sjobeck
- Department of Psychology, University of Virginia, Charlottesville, USA
| | - Evelien Snippe
- Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
| | - Timothy J Trull
- Department of Psychological Sciences, University of Missouri, Columbia, USA
| | - Wolfgang Tschacher
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Date C van der Veen
- Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
| | - Marieke Wichers
- Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
| | - Phillip K Wood
- Department of Psychological Sciences, University of Missouri, Columbia, USA
| | - William C Woods
- Department of Psychology, University of Pittsburgh, Pittsburgh, USA
| | - Aidan G C Wright
- Department of Psychology, University of Pittsburgh, Pittsburgh, USA
| | - Casper J Albers
- Department of Psychology, University of Groningen, Groningen, the Netherlands
| | - Laura F Bringmann
- Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands; Department of Psychology, University of Groningen, Groningen, the Netherlands.
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Burger J, van der Veen DC, Robinaugh DJ, Quax R, Riese H, Schoevers RA, Epskamp S. Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis. BMC Med 2020. [PMID: 32264914 DOI: 10.31234/osf.io/gw2uc] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND The past decades of research have seen an increase in statistical tools to explore the complex dynamics of mental health from patient data, yet the application of these tools in clinical practice remains uncommon. This is surprising, given that clinical reasoning, e.g., case conceptualizations, largely coincides with the dynamical system approach. We argue that the gap between statistical tools and clinical practice can partly be explained by the fact that current estimation techniques disregard theoretical and practical considerations relevant to psychotherapy. To address this issue, we propose that case conceptualizations should be formalized. We illustrate this approach by introducing a computational model of functional analysis, a framework commonly used by practitioners to formulate case conceptualizations and design patient-tailored treatment. METHODS We outline the general approach of formalizing idiographic theories, drawing on the example of a functional analysis for a patient suffering from panic disorder. We specified the system using a series of differential equations and simulated different scenarios; first, we simulated data without intervening in the system to examine the effects of avoidant coping on the development of panic symptomatic. Second, we formalized two interventions commonly used in cognitive behavioral therapy (CBT; exposure and cognitive reappraisal) and subsequently simulated their effects on the system. RESULTS The first simulation showed that the specified system could recover several aspects of the phenomenon (panic disorder), however, also showed some incongruency with the nature of panic attacks (e.g., rapid decreases were not observed). The second simulation study illustrated differential effects of CBT interventions for this patient. All tested interventions could decrease panic levels in the system. CONCLUSIONS Formalizing idiographic theories is promising in bridging the gap between complexity science and clinical practice and can help foster more rigorous scientific practices in psychotherapy, through enhancing theory development. More precise case conceptualizations could potentially improve intervention planning and treatment outcomes. We discuss applications in psychotherapy and future directions, amongst others barriers for systematic theory evaluation and extending the framework to incorporate interactions between individual systems, relevant for modeling social learning processes. With this report, we hope to stimulate future efforts in formalizing clinical frameworks.
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Affiliation(s)
- Julian Burger
- University of Groningen, University Medical Center Groningen, University Center Psychiatry (UCP) Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
- University of Amsterdam, Institute for Advanced Study, Amsterdam, The Netherlands.
| | - Date C van der Veen
- University of Groningen, University Medical Center Groningen, University Center Psychiatry (UCP) Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Donald J Robinaugh
- Harvard University, Department of Psychiatry, Massachusetts General Hospital, .Cambridge, MA, USA
| | - Rick Quax
- University of Amsterdam, Institute for Advanced Study, Amsterdam, The Netherlands
| | - Harriëtte Riese
- University of Groningen, University Medical Center Groningen, University Center Psychiatry (UCP) Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Robert A Schoevers
- University of Groningen, University Medical Center Groningen, University Center Psychiatry (UCP) Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Sacha Epskamp
- University of Amsterdam, Institute for Advanced Study, Amsterdam, The Netherlands
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33
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Burger J, van der Veen DC, Robinaugh DJ, Quax R, Riese H, Schoevers RA, Epskamp S. Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis. BMC Med 2020; 18:99. [PMID: 32264914 PMCID: PMC7333286 DOI: 10.1186/s12916-020-01558-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 03/16/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The past decades of research have seen an increase in statistical tools to explore the complex dynamics of mental health from patient data, yet the application of these tools in clinical practice remains uncommon. This is surprising, given that clinical reasoning, e.g., case conceptualizations, largely coincides with the dynamical system approach. We argue that the gap between statistical tools and clinical practice can partly be explained by the fact that current estimation techniques disregard theoretical and practical considerations relevant to psychotherapy. To address this issue, we propose that case conceptualizations should be formalized. We illustrate this approach by introducing a computational model of functional analysis, a framework commonly used by practitioners to formulate case conceptualizations and design patient-tailored treatment. METHODS We outline the general approach of formalizing idiographic theories, drawing on the example of a functional analysis for a patient suffering from panic disorder. We specified the system using a series of differential equations and simulated different scenarios; first, we simulated data without intervening in the system to examine the effects of avoidant coping on the development of panic symptomatic. Second, we formalized two interventions commonly used in cognitive behavioral therapy (CBT; exposure and cognitive reappraisal) and subsequently simulated their effects on the system. RESULTS The first simulation showed that the specified system could recover several aspects of the phenomenon (panic disorder), however, also showed some incongruency with the nature of panic attacks (e.g., rapid decreases were not observed). The second simulation study illustrated differential effects of CBT interventions for this patient. All tested interventions could decrease panic levels in the system. CONCLUSIONS Formalizing idiographic theories is promising in bridging the gap between complexity science and clinical practice and can help foster more rigorous scientific practices in psychotherapy, through enhancing theory development. More precise case conceptualizations could potentially improve intervention planning and treatment outcomes. We discuss applications in psychotherapy and future directions, amongst others barriers for systematic theory evaluation and extending the framework to incorporate interactions between individual systems, relevant for modeling social learning processes. With this report, we hope to stimulate future efforts in formalizing clinical frameworks.
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Affiliation(s)
- Julian Burger
- University of Groningen, University Medical Center Groningen, University Center Psychiatry (UCP) Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
- University of Amsterdam, Institute for Advanced Study, Amsterdam, The Netherlands.
| | - Date C van der Veen
- University of Groningen, University Medical Center Groningen, University Center Psychiatry (UCP) Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Donald J Robinaugh
- Harvard University, Department of Psychiatry, Massachusetts General Hospital, .Cambridge, MA, USA
| | - Rick Quax
- University of Amsterdam, Institute for Advanced Study, Amsterdam, The Netherlands
| | - Harriëtte Riese
- University of Groningen, University Medical Center Groningen, University Center Psychiatry (UCP) Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Robert A Schoevers
- University of Groningen, University Medical Center Groningen, University Center Psychiatry (UCP) Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Sacha Epskamp
- University of Amsterdam, Institute for Advanced Study, Amsterdam, The Netherlands
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34
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Isvoranu AM, Guloksuz S, Epskamp S, van Os J, Borsboom D. Toward incorporating genetic risk scores into symptom networks of psychosis. Psychol Med 2020; 50:636-643. [PMID: 30867074 PMCID: PMC7093319 DOI: 10.1017/s003329171900045x] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 02/14/2019] [Accepted: 02/18/2019] [Indexed: 11/16/2022]
Abstract
BACKGROUND Psychosis spectrum disorder is a heterogeneous, multifactorial clinical phenotype, known to have a high heritability, only a minor portion of which can be explained by molecular measures of genetic variation. This study proposes that the identification of genetic variation underlying psychotic disorder may have suffered due to issues in the psychometric conceptualization of the phenotype. Here we aim to open a new line of research into the genetics of mental disorders by explicitly incorporating genes into symptom networks. Specifically, we investigate whether links between a polygenic risk score (PRS) for schizophrenia and measures of psychosis proneness can be identified in a network model. METHODS We analyzed data from n = 2180 subjects (controls, patients diagnosed with a non-affective psychotic disorder, and the first-degree relatives of the patients). A network structure was computed to examine associations between the 42 symptoms of the Community Assessment of Psychic Experiences (CAPE) and the PRS for schizophrenia. RESULTS The resulting network shows that the PRS is directly connected to the spectrum of positive and depressive symptoms, with the items conspiracy and no future being more often located on predictive pathways from PRS to other symptoms. CONCLUSIONS To our knowledge, the current exploratory study provides a first application of the network framework to the field of behavior genetics research. This allows for a novel outlook on the investigation of the relations between genome-wide association study-based PRSs and symptoms of mental disorders, by focusing on the dependencies among variables.
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Affiliation(s)
- Adela-Maria Isvoranu
- Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Sinan Guloksuz
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Sacha Epskamp
- Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Jim van Os
- Utrecht University Medical Centre, Utrecht, The Netherlands
| | - Denny Borsboom
- Department of Psychology, Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
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Abstract
Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)-an undirected network model of partial correlations-between observed variables of cross-sectional data or single-subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the ts-lvgvar when estimated from time-series data and the panel-lvgvar when estimated from panel data. These methods have been implemented in the software package psychonetrics, which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rests on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.
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Affiliation(s)
- Sacha Epskamp
- Department of Psychology: Psychological Methods Groups, University of Amsterdam, PO Box 15906, 1001 NK, Amsterdam, The Netherlands.
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36
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Bringmann LF, Elmer T, Epskamp S, Krause RW, Schoch D, Wichers M, Wigman JTW, Snippe E. What do centrality measures measure in psychological networks? J Abnorm Psychol 2019; 128:892-903. [PMID: 31318245 DOI: 10.13140/rg.2.2.25024.58884] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Centrality indices are a popular tool to analyze structural aspects of psychological networks. As centrality indices were originally developed in the context of social networks, it is unclear to what extent these indices are suitable in a psychological network context. In this article we critically examine several issues with the use of the most popular centrality indices in psychological networks: degree, betweenness, and closeness centrality. We show that problems with centrality indices discussed in the social network literature also apply to the psychological networks. Assumptions underlying centrality indices, such as presence of a flow and shortest paths, may not correspond with a general theory of how psychological variables relate to one another. Furthermore, the assumptions of node distinctiveness and node exchangeability may not hold in psychological networks. We conclude that, for psychological networks, betweenness and closeness centrality seem especially unsuitable as measures of node importance. We therefore suggest three ways forward: (a) using centrality measures that are tailored to the psychological network context, (b) reconsidering existing measures of importance used in statistical models underlying psychological networks, and (c) discarding the concept of node centrality entirely. Foremost, we argue that one has to make explicit what one means when one states that a node is central, and what assumptions the centrality measure of choice entails, to make sure that there is a match between the process under study and the centrality measure that is used. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Affiliation(s)
| | - Timon Elmer
- Department of Humanities, Social and Political Sciences
| | | | | | | | - Marieke Wichers
- Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE)
| | | | - Evelien Snippe
- Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE)
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37
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Bringmann LF, Elmer T, Epskamp S, Krause RW, Schoch D, Wichers M, Wigman JTW, Snippe E. What do centrality measures measure in psychological networks? Journal of Abnormal Psychology 2019; 128:892-903. [DOI: 10.1037/abn0000446] [Citation(s) in RCA: 287] [Impact Index Per Article: 57.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Abacioglu CS, Isvoranu AM, Verkuyten M, Thijs J, Epskamp S. Exploring multicultural classroom dynamics: A network analysis. J Sch Psychol 2019; 74:90-105. [PMID: 31213234 DOI: 10.1016/j.jsp.2019.02.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 07/26/2018] [Accepted: 02/11/2019] [Indexed: 10/26/2022]
Abstract
Students' relationships with peers and teachers strongly influence their motivation to engage in learning activities. Ethnic minority students, however, are often victimized in schools, and their educational achievement lags behind that of their majority group counterparts. The aim of the present study was to explore teachers' multicultural approach within their classrooms as a possible factor of influence over students' peer relationships and motivation. We utilized the novel methodology of estimating psychological networks in order to map out the interactions between these constructs within multicultural classrooms. Results indicate that a multicultural approach is directly connected to student motivation for both ethnic majority and minority students. Social integration within peer groups, however, seems to be a possible mediator of this relationship for the ethnic minority students. Due to the hypothesis generating nature of the psychological network approach, a more thorough investigation of this generated mediation hypothesis is called for.
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Affiliation(s)
- Ceren Su Abacioglu
- Department of Child Development and Education, Educational Sciences, University of Amsterdam, Nieuwe Achtergracht 127, 1018 WS Amsterdam, the Netherlands.
| | - Adela-Maria Isvoranu
- Department of Psychology, Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129, 1018 WS Amsterdam, the Netherlands.
| | - Maykel Verkuyten
- Department of Interdisciplinary Social Science, Utrecht University, Padualaan 14, 3584 CH Utrecht, the Netherlands.
| | - Jochem Thijs
- Department of Interdisciplinary Social Science, Utrecht University, Padualaan 14, 3584 CH Utrecht, the Netherlands.
| | - Sacha Epskamp
- Department of Psychology, Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129, 1018 WS Amsterdam, the Netherlands.
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Abstract
Methodological developments and software implementations are progressing at an increasingly fast pace. The introduction and widespread acceptance of preprint archived reports and open-source software have made state-of-the-art statistical methods readily accessible to researchers. At the same time, researchers are increasingly concerned that their results should be reproducible (i.e., the same analysis should yield the same numeric results at a later time), which is a basic requirement for assessing the results’ replicability (i.e., whether results at a later time support the same conclusions). Although this age of fast-paced methodology greatly facilitates reproducibility and replicability, it also undermines them in ways not often realized by researchers. This article draws researchers’ attention to these threats and proposes guidelines to help minimize their impact. Reproducibility may be influenced by software development and change over time, a problem that is greatly compounded by the rising dependency between software packages. Replicability is affected by rapidly changing standards, researcher degrees of freedom, and possible bugs or errors in code, whether introduced by software developers or empirical researchers implementing an analysis. This article concludes with a list of recommendations to improve the reproducibility and replicability of results.
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Oreel TH, Borsboom D, Epskamp S, Hartog ID, Netjes JE, Nieuwkerk PT, Henriques JP, Scherer-Rath M, van Laarhoven HW, Sprangers MA. The dynamics in health-related quality of life of patients with stable coronary artery disease were revealed: a network analysis. J Clin Epidemiol 2019; 107:116-123. [DOI: 10.1016/j.jclinepi.2018.11.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 11/12/2018] [Accepted: 11/27/2018] [Indexed: 12/20/2022]
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41
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Love J, Selker R, Marsman M, Jamil T, Dropmann D, Verhagen J, Ly A, Gronau QF, Smíra M, Epskamp S, Matzke D, Wild A, Knight P, Rouder JN, Morey RD, Wagenmakers EJ. JASP: Graphical Statistical Software for Common Statistical Designs. J Stat Softw 2019. [DOI: 10.18637/jss.v088.i02] [Citation(s) in RCA: 200] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
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42
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Costantini G, Richetin J, Preti E, Casini E, Epskamp S, Perugini M. Stability and variability of personality networks. A tutorial on recent developments in network psychometrics. Personality and Individual Differences 2019. [DOI: 10.1016/j.paid.2017.06.011] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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Abstract
Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on posttraumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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Affiliation(s)
- Sacha Epskamp
- Department of Psychological Methods, University of Amsterdam
| | - Eiko I Fried
- Department of Psychological Methods, University of Amsterdam
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44
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Abstract
Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on posttraumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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Affiliation(s)
- Sacha Epskamp
- Department of Psychological Methods, University of Amsterdam
| | - Eiko I Fried
- Department of Psychological Methods, University of Amsterdam
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45
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Fonseca-Pedrero E, Ortuño J, Debbané M, Chan RCK, Cicero D, Zhang LC, Brenner C, Barkus E, Linscott RJ, Kwapil T, Barrantes-Vidal N, Cohen A, Raine A, Compton MT, Tone EB, Suhr J, Inchausti F, Bobes J, Fumero A, Giakoumaki S, Tsaousis I, Preti A, Chmielewski M, Laloyaux J, Mechri A, Aymen Lahmar M, Wuthrich V, Larøi F, Badcock JC, Jablensky A, Isvoranu AM, Epskamp S, Fried EI. The Network Structure of Schizotypal Personality Traits. Schizophr Bull 2018; 44:S468-S479. [PMID: 29684178 PMCID: PMC6188518 DOI: 10.1093/schbul/sby044] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Elucidating schizotypal traits is important if we are to understand the various manifestations of psychosis spectrum liability and to reliably identify individuals at high risk for psychosis. The present study examined the network structures of (1) 9 schizotypal personality domains and (2) 74 individual schizotypal items, and (3) explored whether networks differed across gender and culture (North America vs China). The study was conducted in a sample of 27001 participants from 12 countries and 21 sites (M age = 22.12; SD = 6.28; 37.5% males). The Schizotypal Personality Questionnaire (SPQ) was used to assess 74 self-report items aggregated in 9 domains. We used network models to estimate conditional dependence relations among variables. In the domain-level network, schizotypal traits were strongly interconnected. Predictability (explained variance of each node) ranged from 31% (odd/magical beliefs) to 55% (constricted affect), with a mean of 43.7%. In the item-level network, variables showed relations both within and across domains, although within-domain associations were generally stronger. The average predictability of SPQ items was 27.8%. The network structures of men and women were similar (r = .74), node centrality was similar across networks (r = .90), as was connectivity (195.59 and 199.70, respectively). North American and Chinese participants networks showed lower similarity in terms of structure (r = 0.44), node centrality (r = 0.56), and connectivity (180.35 and 153.97, respectively). In sum, the present article points to the value of conceptualizing schizotypal personality as a complex system of interacting cognitive, emotional, and affective characteristics.
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Affiliation(s)
- Eduardo Fonseca-Pedrero
- Department of Educational Sciences, University of La Rioja, La Rioja, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Oviedo, Spain
| | - Javier Ortuño
- Department of Educational Sciences, University of La Rioja, La Rioja, Spain
| | - Martin Debbané
- Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - David Cicero
- Department of Psychology, University of Hawaii at Manoa
| | - Lisa C Zhang
- Department of Psychology, University of British Columbia, Canada
| | - Colleen Brenner
- Department of Psychology, University of British Columbia, Canada
| | - Emma Barkus
- School of Psychology, University of Wollongong, Wollongong, Australia
| | | | - Thomas Kwapil
- Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC
| | - Neus Barrantes-Vidal
- Department of Clinical and Health Psychology, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Alex Cohen
- Department of Psychology, Louisiana State University, Louisiana, LA
| | - Adrian Raine
- Department of Criminology, University of Pennsylvania
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
- Department of Psychology, University of Pennsylvania
| | | | - Erin B Tone
- Department of Psychology, Georgia State University, Atlanta, GA
| | - Julie Suhr
- Department of Psychology, Ohio University Athens, OH
| | - Felix Inchausti
- Department of Medicine, University of Navarra, Pamplona, Spain
| | - Julio Bobes
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Oviedo, Spain
- Department of Psychiatry, University of Oviedo, Oviedo, Spain
| | - Axit Fumero
- Department of Psychology, University of La Laguna, Tenerife, Spain
| | | | | | | | | | - Julien Laloyaux
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- NORMENT—Norwegian Center of Excellence for Mental Disorders Research, University of Oslo, Oslo, Norway
- Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège, Belgium
| | - Anwar Mechri
- Psychiatry Department, University Hospital of Monastir, Monastir, Tunisia
| | | | - Viviana Wuthrich
- Centre for Emotional Health, Department of Psychology, Macquarie University, Sydney, Australia
| | - Frank Larøi
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- NORMENT—Norwegian Center of Excellence for Mental Disorders Research, University of Oslo, Oslo, Norway
- Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège, Belgium
| | - Johanna C Badcock
- Centre for Clinical Research in Neuropsychiatry, School of Psychiatry and Clinical Neurosciences, University of Western Australia, Perth, Australia
| | - Assen Jablensky
- Centre for Clinical Research in Neuropsychiatry, School of Psychiatry and Clinical Neurosciences, University of Western Australia, Perth, Australia
| | - Adela M Isvoranu
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Sacha Epskamp
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Eiko I Fried
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
- Department of Clinical Psychology, Leiden University, Leiden, The Netherlands
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46
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Abstract
BACKGROUND Conceptualizing posttraumatic stress disorder (PTSD) symptoms as a dynamic system of causal elements could provide valuable insights into the way that PTSD develops and is maintained in traumatized individuals. We present the first study to apply a multilevel network model to produce an exploratory empirical conceptualization of dynamic networks of PTSD symptoms, using data collected during a period of conflict. METHODS Intensive longitudinal assessment data were collected during the Israel-Gaza War in July-August 2014. The final sample (n = 96) comprised a general population sample of Israeli adult civilians exposed to rocket fire. Participants completed twice-daily reports of PTSD symptoms via smartphone for 30 days. We used a multilevel vector auto-regression model to produce contemporaneous and temporal networks, and a partial correlation network model to obtain a between-subjects network. RESULTS Multilevel network analysis found strong positive contemporaneous associations between hypervigilance and startle response, avoidance of thoughts and avoidance of reminders, and between flashbacks and emotional reactivity. The temporal network indicated the central role of startle response as a predictor of future PTSD symptomatology, together with restricted affect, blame, negative emotions, and avoidance of thoughts. There were some notable differences between the temporal and contemporaneous networks, including the presence of a number of negative associations, particularly from blame. The between-person network indicated flashbacks and emotional reactivity to be the most central symptoms. CONCLUSIONS This study suggests various symptoms that could potentially be driving the development of PTSD. We discuss clinical implications such as identifying particular symptoms as targets for interventions.
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Affiliation(s)
- Talya Greene
- Department of Community Mental Health,University of Haifa,Haifa,Israel
| | - Marc Gelkopf
- Department of Community Mental Health,University of Haifa,Haifa,Israel
| | - Sacha Epskamp
- Department of Psychology,University of Amsterdam,Amsterdam,The Netherlands
| | - Eiko Fried
- Department of Psychology,University of Amsterdam,Amsterdam,The Netherlands
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47
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Abstract
We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.
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Affiliation(s)
- Sacha Epskamp
- a Department of Psychological Methods , University of Amsterdam
| | | | - René Mõttus
- b Department of Psychology , University of Edinburgh
| | - Denny Borsboom
- a Department of Psychological Methods , University of Amsterdam
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48
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Mõttus R, Condon D, Wood D, Epskamp S. Call for Papers. European Journal of Psychological Assessment 2018. [DOI: 10.1027/1015-5759/a000493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- René Mõttus
- Department of Psychology, The University of Edinburgh, UK
| | - David Condon
- Department of Medical Social Sciences, Northwestern University, Chicago, IL, USA
| | - Dustin Wood
- Department of Management, University of Alabama, AL, USA
| | - Sacha Epskamp
- Department of Psychological Methods, University of Amsterdam, The Netherlands
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49
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Epskamp S, van Borkulo CD, van der Veen DC, Servaas MN, Isvoranu AM, Riese H, Cramer AOJ. Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections. Clin Psychol Sci 2018; 6:416-427. [PMID: 29805918 PMCID: PMC5952299 DOI: 10.1177/2167702617744325] [Citation(s) in RCA: 181] [Impact Index Per Article: 30.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2017] [Accepted: 10/25/2017] [Indexed: 12/30/2022]
Abstract
Recent literature has introduced (a) the network perspective to psychology and (b) collection of time series data to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intraindividual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a temporal network, in which one investigates if symptoms (or other relevant variables) predict one another over time, and a contemporaneous network, in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time series data. We explain the importance of partial correlation networks and exemplify the network structures on time series data of a psychiatric patient.
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Affiliation(s)
- Sacha Epskamp
- Department of Psychological Methods,
University of Amsterdam
| | | | - Date C. van der Veen
- Department of Psychiatry,
Interdisciplinary Center for Psychopathology and Emotion Regulation, University
Medical Center Groningen, University of Groningen
| | - Michelle N. Servaas
- Neuroimaging Center, Department of
Neuroscience, University of Groningen, University Medical Center Groningen
| | | | - Harriëtte Riese
- Department of Psychiatry,
Interdisciplinary Center for Psychopathology and Emotion Regulation, University
Medical Center Groningen, University of Groningen
| | - Angélique O. J. Cramer
- Neuroimaging Center, Department of
Neuroscience, University of Groningen, University Medical Center Groningen
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50
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Abstract
Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on posttraumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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Affiliation(s)
- Sacha Epskamp
- Department of Psychological Methods, University of Amsterdam
| | - Eiko I Fried
- Department of Psychological Methods, University of Amsterdam
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