scholarly journals A tutorial on Bayesian Networks for psychopathology researchers

2021 ◽  
Author(s):  
Giovanni Briganti ◽  
Marco Scutari ◽  
Richard J. McNally
Keyword(s):  
Bayesian Networks ◽  
Graphical Models ◽  
Conditional Independence ◽  
Directed Acyclic Graph ◽  
Partial Correlation ◽  
Causal Effects ◽  
Cross Sectional ◽  
Data Set ◽  
Correlation Networks ◽  
Common Problems

Bayesian Networks are probabilistic graphical models that represent conditional independence relationships among variables as a directed acyclic graph (DAG), where edges can be interpreted as causal effects connecting one causal symptom to an effect symptom. These models can help overcome one of the key limitations of partial correlation networks whose edges are undirected. This tutorial aims to introduce Bayesian Networks to identify admissible causal relationships in cross-sectional data, as well as how to estimate these models in R through three algorithm families with an empirical example data set of depressive symptoms. In addition, we discuss common problems and questions related to Bayesian networks. We recommend Bayesian networks be investigated to gain causal insight in psychological data.

scholarly journals On Accepting the Null Hypothesis of Conditional Independence in Partial Correlation Networks: A Bayesian Analysis

2021 ◽  
Author(s):  
Donald Ray Williams ◽  
Giovanni Briganti ◽  
Paul Linkowski ◽  
Joris Mulder
Keyword(s):  
Traumatic Stress ◽  
Null Hypothesis ◽  
Conditional Independence ◽  
Partial Correlation ◽  
Stress Disorder ◽  
P Value ◽  
Correlation Networks ◽  
Partial Correlations ◽  
Disorder Symptom ◽  
Shed Light

Partial correlation networks have emerged as an increasingly popular model for studyingmental disorders. Although conditional independence is a fundamental concept in networkanalysis, which corresponds to the null hypothesis, the focus is typically to detect and thenvisualize non-zero partial correlations (i.e., the “edges” connecting nodes) in a graph. As aresult, it may be tempting to interpret a missing edge as providing evidence for itsabsence—analogously to misinterpreting a non-significant p-value. In this work, we firstestablish that a missing edge is incorrectly interpreted as providing evidence for conditionalindependence, with examples spanning from substantive applications to tutorials thatinstruct researchers to misinterpret their networks. We then go beyond misguided“inferences” and establish that null associations are interesting in their own right. In thefollowing section, three illustrative examples are provided that employ Bayesian hypothesistesting to formally evaluate the null hypothesis, including a reanalysis of twopsychopathology networks, confirmatory testing to determine whether a particularpost-traumatic stress disorder symptom is disconnected from the network, and attenuationdue to correcting for covariates. Our results shed light upon conditionally independentsymptoms and demonstrate that a missing edge does not necessarily correspond toevidence for the null hypothesis. These findings are accompanied with a simulation studythat provides insights into the sample size needed to accurately detect null relations. Weconclude with implications for both clinical to theoretical inquiries.

scholarly journals Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections

2017 ◽  
Author(s):  
Sacha Epskamp ◽  
Claudia van Borkulo ◽  
Date C. van der Veen ◽  
Michelle Servaas ◽  
Adela-Maria Isvoranu ◽  
...  
Keyword(s):  
Time Series ◽  
Partial Correlation ◽  
Time Series Data ◽  
Recent Literature ◽  
Series Data ◽  
Network Structures ◽  
Cross Sectional ◽  
Correlation Networks ◽  
Relevant Variables ◽  
Analysis Of Time Series

Recent literature has introduced (1) the network perspective to psychology, and (2) collection of time-series data in order to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intra-individual 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.

scholarly journals Bayesian Hypothesis Testing for Gaussian Graphical Models:Conditional Independence and Order Constraints

2019 ◽  
Author(s):  
Donald Ray Williams ◽  
Joris Mulder
Keyword(s):  
Graphical Models ◽  
Partial Correlation ◽  
R Package ◽  
The Novel ◽  
Gaussian Graphical Models ◽  
Conditional Dependence ◽  
Correlation Networks ◽  
The Social ◽  
Partial Correlations ◽  
Order Constraints

Gaussian graphical models (GGM; partial correlation networks) have become increasingly popular in the social and behavioral sciences for studying conditional (in)dependencies between variables. In this work, we introduce exploratory and confirmatory Bayesian tests for partial correlations. For the former, we first extend the customary GGM formulation that focuses on conditional dependence to also consider the null hypothesis of conditional independence for each partial correlation. Here a novel testing strategy is introduced that can provide evidence for a null, negative, or positive effect. We then introduce a test for hypotheses with order constraints on partial correlations. This allows for testing theoretical and clinical expectations in GGMs. The novel matrix$-F$ prior distribution is described that provides increased flexibility in specification compared to the Wishart prior. The methods are applied to PTSD symptoms. In several applications, we demonstrate how the exploratory and confirmatory approaches can work in tandem: hypotheses are formulated from an initial analysis and then tested in an independent dataset. The methodology is implemented in the R package BGGM.

scholarly journals Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections

2018 ◽  
Vol 6 (3) ◽  
pp. 416-427 ◽  
Author(s):  
Sacha Epskamp ◽  
Claudia D. van Borkulo ◽  
Date C. van der Veen ◽  
Michelle N. Servaas ◽  
Adela-Maria Isvoranu ◽  
...  
Keyword(s):  
Time Series ◽  
Partial Correlation ◽  
Time Series Data ◽  
Recent Literature ◽  
Series Data ◽  
Network Structures ◽  
Cross Sectional ◽  
Correlation Networks ◽  
Relevant Variables ◽  
Analysis Of Time Series

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.

scholarly journals The exploratory value of cross-sectional partial correlation networks: Predicting relationships between change trajectories in borderline personality disorder

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254496
Author(s):  
Lino von Klipstein ◽  
Denny Borsboom ◽  
Arnoud Arntz
Keyword(s):  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Partial Correlation ◽  
Severity Index ◽  
Borderline Personality ◽  
Causal Mechanism ◽  
Change Trajectories ◽  
Cross Sectional ◽  
Correlation Networks ◽  
Within Subjects

Objective Within the network approach to psychopathology, cross-sectional partial correlation networks have frequently been used to estimate relationships between symptoms. The resulting relationships have been used to generate hypotheses about causal links between symptoms. In order to justify such exploratory use of partial correlation networks, one needs to assume that the between-subjects relationships in the network approximate systematic within-subjects relationships, which are in turn the results of some within-subjects causal mechanism. If this assumption holds, relationships in the network should be mirrored by relationships between symptom changes; if links in networks approximate systematic within-subject relationships, change in a symptom should relate to change in connected symptoms. Method To investigate this implication, we combined longitudinal data on the Borderline Personality Disorder Severity Index from four samples of borderline personality disorder patients (N = 683). We related parameters from baseline partial correlation networks of symptoms to relationships between change trajectories of these symptoms. Results Across multiple levels of analysis, our results showed that parameters from baseline partial correlation networks are strongly predictive of relationships between change trajectories. Conclusions By confirming its implication, our results support the idea that cross-sectional partial correlation networks hold a relevant amount of information about systematic within-subjects relationships and thereby have exploratory value to generate hypotheses about the causal dynamics between symptoms.

Suicide Ideation and Attempt in a Community Cohort of Urban Aboriginal Youth: A Cross-Sectional Study

Crisis ◽  
2013 ◽  
Vol 34 (4) ◽  
pp. 251-261 ◽  
Author(s):  
Joanne N. Luke ◽  
Ian P. Anderson ◽  
Graham J. Gee ◽  
Reg Thorpe ◽  
Kevin G. Rowley ◽  
...  
Keyword(s):  
Health Service ◽  
Indigenous People ◽  
Suicide Ideation ◽  
Aboriginal Health ◽  
Cross Sectional ◽  
Data Set ◽  
Indigenous Youth ◽  
Aboriginal Youth ◽  
Social Distress ◽  
Suicide Behavior

Background: There has been increasing attention over the last decade on the issue of indigenous youth suicide. A number of studies have documented the high prevalence of suicide behavior and mortality in Australia and internationally. However, no studies have focused on documenting the correlates of suicide behavior for indigenous youth in Australia. Aims: To examine the prevalence of suicide ideation and attempt and the associated factors for a community 1 The term ”community” refers specifically to Koori people affiliated with the Victorian Aboriginal Health Service. cohort of Koori 2 The term ”Koori” refers to indigenous people from the south-eastern region of Australia, including Melbourne. The term ”Aboriginal” has been used when referring to indigenous people from Australia. The term ”indigenous” has been used throughout this article when referring to the first people of a nation within an international context. (Aboriginal) youth. Method: Data were obtained from the Victorian Aboriginal Health Service (VAHS) Young People’s Project (YPP), a community initiated cross-sectional data set. In 1997/1998, self-reported data were collected for 172 Koori youth aged 12–26 years living in Melbourne, Australia. The data were analyzed to assess the prevalence of current suicide ideation and lifetime suicide attempt. Principal components analysis (PCA) was used to identify closely associated social, emotional, behavioral, and cultural variables at baseline and Cox regression modeling was then used to identify associations between PCA components and suicide ideation and attempt. Results: Ideation and attempt were reported at 23.3% and 24.4%, respectively. PCA yielded five components: (1) emotional distress, (2) social distress A, (3) social distress B, (4) cultural connection, (5) behavioral. All were positively and independently associated with suicide ideation and attempt, while cultural connection showed a negative association. Conclusions: Suicide ideation and attempt were common in this cross-section of indigenous youth with an unfavorable profile for the emotional, social, cultural, and behavioral factors.

Bereavement or Breakup: A Network Approach to Modeling Differential Pathways into Depression

2019 ◽  
Author(s):  
Julian Burger ◽  
Margaret S. Stroebe ◽  
Pasqualina Perrig-Chiello ◽  
Henk A.W. Schut ◽  
Stefanie Spahni ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Graphical Models ◽  
Contextual Factors ◽  
Marital Disruption ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
Multiple Time ◽  
Cross Sectional ◽  
Network Analyses ◽  
Within Subjects

Background: Prior network analyses demonstrated that the death of a loved one potentially precedes specific depression symptoms, primarily loneliness, which in turn links to other depressive symptoms. In this study, we extend prior research by comparing depression symptom network structures following two types of marital disruption: bereavement versus separation. Methods: We fitted two Gaussian Graphical Models to cross-sectional data from a Swiss survey of older persons (145 bereaved, 217 separated, and 362 married controls), and compared symptom levels across bereaved and separated individuals. Results: Separated compared to widowed individuals were more likely to perceive an unfriendly environment and oneself as a failure. Both types of marital disruption were linked primarily to loneliness, from where different relations emerged to other depressive symptoms. Amongst others, loneliness had a stronger connection to perceiving oneself as a failure in separated compared to widowed individuals. Conversely, loneliness had a stronger connection to getting going in widowed individuals. Limitations: Analyses are based on cross-sectional between-subjects data, and conclusions regarding dynamic processes on the within-subjects level remain putative. Further, some of the estimated parameters in the network exhibited overlapping confidence intervals and their order needs to be interpreted with care. Replications should thus aim for studies with multiple time points and larger samples. Conclusions: The findings of this study add to a growing body of literature indicating that depressive symptom patterns depend on contextual factors. If replicated on the within-subjects level, such findings have implications for setting up patient-tailored treatment approaches in dependence of contextual factors.

Prevention

2017 ◽  
Author(s):  
C. Barr Taylor ◽  
Ellen E. Fitzsimmons-Craft ◽  
Neha J. Goel
Keyword(s):  
Cognitive Dissonance ◽  
Media Literacy ◽  
Empirical Support ◽  
Clinical Risk Factor ◽  
Prevention Research ◽  
Cross Sectional ◽  
School Based ◽  
Theoretical Approaches ◽  
Common Problems ◽  
Obesity Prevention Programs

Eating disorders (EDs) are important and common problems among adolescents and young women, and preventing them would be an important public health achievement. Fortunately, several recent studies, informed by cross-sectional, longitudinal, and clinical risk factor research, have demonstrated a significant decrease in ED risk factors, with several programs also achieving a significant reduction in ED onset within at-risk females. This chapter reviews and evaluates the state of ED prevention research, highlighting current theoretical approaches and effective programs, emphasizing emerging empirical support for cognitive dissonance, Internet, school-based, media literacy, and combined ED and obesity prevention programs. Conclusions about how to enhance recent progress in the field of EDs are provided.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

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