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Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality

Network analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Cont...

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Detalles Bibliográficos
Autores principales: Ryan, Oisín, Hamaker, Ellen L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021117/
https://www.ncbi.nlm.nih.gov/pubmed/34165691
http://dx.doi.org/10.1007/s11336-021-09767-0
Descripción
Sumario:Network analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper, we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11336-021-09767-0.