By Denny Borsboom, Eiko Fried, Sacha Epskamp, Lourens Waldorp, Claudia van Borkulo, Han van der Maas, and Angélique Cramer
In a forthcoming paper in the Journal of Abnormal Psychology entitled “Evidence that psychopathology networks do not replicate”, Miriam Forbes, Aidan Wright, Kristian Markon, and Robert Krueger purport to show that network structures do not replicate across datasets. They estimate networks for symptoms of Major Depressive Episode (MDE) and Generalized Anxiety Disorder (GAD) in two large datasets – one from the National Comorbidity Survey-Replication (NCS) and one from the Australian National Survey of Mental Health and Well-Being (NSMHWB). As is evident from our published work (Fried & Cramer, in press; Fried, van Borkulo, Cramer, Boschloo, Schoevers, & Borsboom, 2017; Epskamp, Borsboom, & Fried, 2017) we see the reproducibility of network research as a top priority, and are happy to see that researchers are investigating this important issue.
The conclusion proposed by the authors is that “network analysis […] had poor replicability between […] samples”. This conclusion is not supported by the data for state-of-the-art network models, as we will argue in a commentary solicited by the Journal of Abnormal Psychology. Given the intense interest in the matter, however, we deemed it useful to post a short blog post in advance to state a fact that may not be obvious to most readers given the rhetorical style of Forbes et al. (in press). In one sentence: state-of-the-art networks don’t just replicate – they replicate with stunning precision.
Unfortunately, the authors of the paper did not share their data or code with us yet, so we cannot fully evaluate their work, but they did share the parameter matrices that they got out of the analysis, and these are sufficient to establish that the authors’ conclusion does not apply to state-of-the-art network modeling techniques (e.g., networks estimated using our R-package IsingFit; Van Borkulo et al., 2014). The authors of the paper suggest as much when they say that “[t]he replicability of the edges in the Ising models was remarkably similar between and within samples”, but this conclusion is easily lost in the rhetoric of the paper’s title, abstract, and discussion. In addition, the authors insufficiently articulate just how similar the networks are; as a result, users of our techniques may be wondering whether Ising networks really live up to their reputation as highly stable and secure network estimation techniques.
Figure 1. The networks estimated from the NCS and NSMHWB samples, and the scatterplot of network parameters as estimated in both samples (r=.95). The network representations use the default settings of qgraph (Epskamp, Cramer, Waldorp, Schmittmann, & Borsboom, 2011) and a common (average) layout to optimize the comparison between datasets.
So just how replicable are Ising networks? Figure 1 pictures the situation quite clearly: the IsingFit networks are almost indistinguishable, and the network parameters display a whopping correlation of .95 for network edges and .93 for node thresholds across samples (Spearman correlations equal .88 and .85, respectively). Even centrality indices, which we usually approach with considerable caution due to their sensitivity to sampling variation (Epskamp, Borsboom, & Fried, 2017), show surprisingly good replication performance with correlations of .94 (strength), .94 (betweenness), and .76 (closeness).
Nobody in our group had in fact expected such an accurate replication across two entirely distinct samples. As such, we argue that the authors’ conclusion that “the unique utility of network analysis …seems limited to visualizing complex multivariate relationships…” is unwarranted. Given our re-analysis of the results of the Forbes et al. (in press) paper one will wonder: how on earth can the authors of the paper interpret this result as “evidence that psychopathology networks do not replicate”? Well, if you want to find that out, keep an eye out for the upcoming issue of the Journal of Abnormal Psychology, in which we will provide a comprehensive dissection of their methodology and argumentation. We’ll keep you posted!
Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48, 1-18.
Fried, E. I.*, van Borkulo, C. D.*, Cramer, A. O. J., Lynn, B., Schoevers, R. A., Borsboom, D. (2016). Mental disorders as networks of problems: a review of recent insights. Social Psychiatry and Psychiatric Epidemiology, 52, 1-10.
Van Borkulo, C.D., Borsboom, D., Epskamp, S., Blanken, T.F., Boschloo, L., Schoevers, R.A. & Waldorp, L.J. (2014). A new method for constructing networks from binary data. Scientific Reports, 4: 5918. doi: 10.1038/srep05918