Open AccessRobustness and replicability of psychopathology networks
Author(s) -
Borsboom Denny,
Robinaugh Donald J.,
Rhemtulla Mijke,
Cramer Angélique O.J.
Publication year - 2018
Publication title -
world psychiatry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 15.51
H-Index - 93
eISSN - 2051-5545
pISSN - 1723-8617
DOI - 10.1002/wps.20515
Subject(s) - computer science , spurious relationship , robustness (evolution) , pairwise comparison , psychopathology , sample size determination , statistical power , machine learning , data mining , statistical model , artificial intelligence , econometrics , statistics , mathematics , medicine , biochemistry , gene , clinical psychology , chemistry
Network approaches to psychopathology hold that mental disorders arise from the interplay between symptoms in a network structure1, 2. In the past few years, statistical techniques that estimate networks were developed and applied to many disorders3. As empirical findings start to accumulate, the question arising is which of these findings are robust and replicable. Here we evaluate the state of psychopathological network research based on three methodological criteria: model quality, precision, and replicability.