11./12. Januar 2018
Pia Tio/Adela Isvoranu (Amsterdam)

Day 1 (10:00–17:00):
• Introduction & Theoretical Foundation of Network Analysis
• Network estimation & inference (how to estimate and interpret networks with Gaussian & Binary variables)
Day 2 (9:00–16:00)
• Network stability: how accurate and stable are the estimated networks
• Advanced methods: e.g. network comparison test, mixed graphical models, latent variable network modeling
Please note that within each block there are both a lecture and a computer practical in R

This workshop is focused on group-level networks: what is the symptom network of a group of patients with, for instance, Major Depression? Day 1 starts with a conceptual introduction to psychopathological networks — in which we explain the main differences between the network framework and alternatives like the common cause model – and an overview of the prior literature organized into disorders (e.g., depression, PTSD, psychosis, substance abuse, etc.) and topics (e.g., centrality, comorbidity, early warning signals). Using packages such as qgraph, bootnet, and IsingFit, we use the free statistical environment R to learn the basics about (1) network estimation and (2) network inference. On Day 2 we will continue with (3) network stability and accuracy. 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, stability and accuracy estimation allows us to gain insight into the robustness of our networks: how likely are they going to be replicated? We conclude Day 2 with advanced methods such as the statistical comparison of networks, the modeling of networks containing different types of variables (mixed graphical models via the R-package mgm), and some considerations on latent variable network modeling.