PresentationPDF Available

2-day Network Analysis Workshop: Estimation, Inference, and Stability of Psychopathological Networks

Authors:

Abstract

(Note that all authors contributed equally to this workshop) The 2-day network workshop starts with a brief introduction into network models, in which we explain the main conceptual differences between the network framework and alternatives like the common cause model. The focus of the first day is on group-level networks: what is the symptom network of a group of patients with, for instance, Major Depression? Using packages such as qgraph, bootnet, and IsingFit, we use the free statistical environment R to learn the basics about (1) network estimation, (2) network inference, and (3) network robustness. Network estimation is concerned with the question which types of models are appropriate for our data, such as the Ising Model for binary data or the Gaussian Graphical Model for metric data. In this section, we also discuss how to apply regularization methods to networks in order to avoid estimating false positive associations. The second topic, network inference, covers graph theoretical measures such as centrality that allow us to interpret networks. What symptoms are most connected and relevant with other symptoms in the causal web? Finally, robustness estimation allows us to gain insight on the stability of our computed networks: how likely are they going to be replicated? We conclude the day with learning how networks can be compared (e.g., across groups, within groups across time). The focus of the second day is on dynamic time-series models: how do symptoms impact on each other over time? After an introduction into the general modeling framework with some substantive examples of recent papers, we learn to estimate the network model – specifically, the vector auto-regressive (VAR) model – for one participant. We then go through the assumptions that the VAR model requires such as stationarity and equidistance of measurement points. After that, we discuss the multilevel extension of the VAR model to the case of a group of participants, using the package mlVAR, followed by a discussion of some common problems and advanced techniques. We conclude the workshop with a FAQ and discussion section.
ResearchGate has not been able to resolve any citations for this publication.
ResearchGate has not been able to resolve any references for this publication.