Network analysis is quickly gaining popularity in psychopathology research as a method that aims to reveal causal relationships among individual symptoms. To date, 4 main types of psychopathology networks have been proposed: (a) association networks, (b) regularized concentration networks, (c) relative importance networks, and (d) directed acyclic graphs. The authors examined the replicability of these analyses based on symptoms of major depression and generalized anxiety between and within 2 highly similar epidemiological samples (i.e., the National Comorbidity Survey-Replication [n = 9282] and the National Survey of Mental Health and Wellbeing [n 8841]). Although association networks were stable, the 3 other types of network analysis (i.e., the conditional independence networks) had poor replicability between and within methods and samples. The detailed aspects of the models-such as the estimation of specific edges and the centrality of individual nodes-were particularly unstable. For example, 44% of the symptoms were estimated as the "most influential" on at least 1 centrality index across the 6 conditional independence networks in the full samples, and only 13-21% of the edges were consistently estimated across these networks. One of the likely reasons for the instability of the networks is the predominance of measurement error in the assessment of individual symptoms. The authors discuss the implications of these findings for the growing field of psychopathology network research, and conclude that novel results originating from psychopathology networks should be held to higher standards of evidence before they are ready for dissemination or implementation in the field.
Bibliographical noteFunding Information:
A subset of the results in this article were presented as a poster at the 2017 American Psychopathological Association meeting in New York, NY. The poster reported the replicability of the edges in the Ising networks (i.e., the proportion of edges that were replicated and that failed to replicate, as well as the change in replicated edge strength) between and within the two samples reported in the present study. Miriam K. Forbes would like to thank Justin Anker for the introduction to-and many discussions about-the world of network analysis. This research was supported in part by a National Institute of Drug Abuse training grant supporting the work of Miriam K. Forbes (T320A037183). Aidan G. C. Wright's efforts were supported by the National Institute of Mental Health (L30 MH101760). The views contained are solely those of the authors and do not necessarily reflect those of the funding source. The National Comorbidity Survey Replication was supported by the National Institute of Mental Health (NIMH; U01-MH60220) with supplemental support from the National Institute of Drug Abuse, the Substance Abuse and Mental Health Services Administration, the Robert Wood Johnson Foundation (Grant 044708), and the John W. Alden Trust. The National Survey of Mental Health and Wellbeing was funded by the Australian National Health Branch of the Commonwealth Department of Health and Aged Care, Under the National Mental Health Strategy. It was conducted by the Australian Bureau of Statistics.
© 2017 American Psychological Association.
- Causal inference
- Network analysis
- Psychopathology networks
- Replication crisis