APS
APS Virtual Poster Showcase
Using Centrality Metrics of Network Models to Understand, Predict, and Control Clinical Outcomes
Network models in cross-sectional and time-series data often estimate the ‘centrality’ (i.e. interconnectedness) of nodes, based on the idea that central nodes are novel treatment targets. We discuss several empirical and methodological projects that determine if centrality guides understanding, predicting, and controlling of clinical outcomes, followed by a critical discussion.
Chairs & Discussants
- Eiko FriedChair
Leiden University - Laura BringmannDiscussant
University of Groningen
Presentations
- Predicting Treatment Outcomes for Depression Using Symptom CentralityCiaran O'Driscoll, Joshua Buckmann
- Central Symptoms and the Relationship between Change in Symptoms and Change in Disorders: Establishing and Critically Evaluating a Robust Empirical FindingDonald Robinaugh
- Controllability Centrality: A Better Way of Identifying Optimal Treatment TargetsTeague Henry
