Skip to main content

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
Cancel
×
×
Home
  • Home
Article
  • Cited by 4
  • Cited by
    Crossref Citations
    Crossref logo
    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Fried, Eiko I. Eidhof, Marloes B. Palic, Sabina Costantini, Giulio Huisman-van Dijk, Hilde M. Bockting, Claudi L. H. Engelhard, Iris Armour, Cherie Nielsen, Anni B. S. and Karstoft, Karen-Inge 2018. Replicability and Generalizability of Posttraumatic Stress Disorder (PTSD) Networks: A Cross-Cultural Multisite Study of PTSD Symptoms in Four Trauma Patient Samples. Clinical Psychological Science, p. 216770261774509.

    von Stockert, Sophia H.H. Fried, Eiko I. Armour, Cherie and Pietrzak, Robert H. 2018. Evaluating the stability of DSM-5 PTSD symptom network structure in a national sample of U.S. military veterans. Journal of Affective Disorders, Vol. 229, p. 63.

    Haslbeck, Jonas M. B. and Waldorp, Lourens J. 2017. How well do network models predict observations? On the importance of predictability in network models. Behavior Research Methods,

    Birkeland, Marianne S. Blix, Ines Solberg, Øivind and Heir, Trond 2017. Gender Differences in Posttraumatic Stress Symptoms after a Terrorist Attack: A Network Approach. Frontiers in Psychology, Vol. 8,

    ×
  • Access
  • Open access

How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets

  • J. M. B. Haslbeck (a1) and E. I. Fried (a1)
Abstract
Background

Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node – its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality.

 Methods

We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art network models to all datasets, and computed the predictability of all nodes.

 Results

Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis.

 Conclusions

Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed.
  • View HTML
      • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets
      Available formats
      ×
Copyright
COPYRIGHT: © Cambridge University Press 2017
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Corresponding author
*Address for correspondence: J. M. B. Haslbeck, University of Amsterdam, Psychological Methods, Nieuwe Achtergracht 129-B, 1018 WT, Amsterdam, The Netherlands. (Email: jonashaslbeck@gmail.com, http://jmbh.github.io )
References
Hide All
Anderson, GM, Montazeri, F, de Bildt, A (2015). Network approach to autistic traits: group and subgroup analyses of ADOS item scores. Journal of Autism and Developmental Disorders 45, 3115–3132.
Armour, C, Fried, EI, Deserno, MK, Tsai, J, Pietrzak, RH (2017). A network analysis of DSM-5 posttraumatic stress disorder symptoms and correlates in US military veterans. Journal of Anxiety Disorders 45, 49–59.
Beard, C, Millner, AJ, Forgeard, MJC, Fried, EI, Hsu, KJ, Treadway, MT, Leonard, CV, Kertz, SJ, Björgvinsson, T (2016). Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychological Medicine 46, 3359.
Borsboom, D, Cramer, AO (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology 9, 91–121.
Boschloo, L, van Borkulo, CD, Borsboom, D, Schoevers, RA (2016 a). A prospective study on how symptoms in a network predict the onset of depression. Psychotherapy and Psychosomatics 85, 183–184.
Boschloo, L, Schoevers, RA, van Borkulo, CD, Borsboom, D, Oldehinkel, AJ (2016 b). The network structure of psychopathology in a community sample of preadolescents. Journal of Abnormal Psychology 125, 599.
Boschloo, L, van Borkulo, CD, Rhemtulla, M, Keyes, KM, Borsboom, D, Schoevers, RA (2015). The network structure of symptoms of the diagnostic and statistical manual of mental disorders. PloS ONE 10, e0137621.
Breiman, L (2001). Statistical modeling: the two cultures (with comments and a rejoinder by the author). Statistical Science 16, 199–231.
Carr, DS, Nesse, RM, Wortman, CB (eds) (2005). Spousal Bereavement in Late Life. Springer Publishing Company: New York.
Cramer, AOJ, Waldorp, LJ, van der Maas, HLJ, Borsboom, D (2010). Comorbidity: a network perspective. Behavioral and Brain Sciences 33, 137–150.
Curtiss, J, Klemanski, DH (2016). Taxonicity and network structure of generalized anxiety disorder and major depressive disorder: an admixture analysis and complex network analysis. Journal of Affective Disorders 199, 99–105.
Deserno, MK, Borsboom, D, Begeer, S, Geurts, HM (2016). Multicausal systems ask for multicausal approaches: a network perspective on subjective well-being in individuals with autism spectrum disorder. Autism, 1–12. doi: 10.1177/1362361316660309.
Epskamp, S, Cramer, AO, Waldorp, LJ, Schmittmann, VD, Borsboom, D (2012). qgraph: network visualizations of relationships in psychometric data. Journal of Statistical Software 48, 1–18.
Epskamp, S, Fried, EI (2016). A Tutorial on Regularized Partial Correlation Networks. arXiv preprint arXiv:1607.01367.
Fried, EI, Bockting, C, Arjadi, R, Borsboom, D, Amshoff, M, Cramer, AO, Epskamp, S, Tuerlinckx, F, Carr, D, Stroebe, M (2015). From loss to loneliness: the relationship between bereavement and depressive symptoms. Journal of Abnormal Psychology 124, 256.
Fried, EI, Cramer, AOJ (2016 b). Moving forward: challenges and directions for psychopathological network theory and methodology. doi: 10.17605/OSF.IO/MH3CF.
Fried, EI, Epskamp, S, Nesse, RM, Tuerlinckx, F, Borsboom, D (2016 c). What are'good'depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. Journal of Affective Disorders 189, 314–320.
Fried, EI, van Borkulo, CD, Cramer, AO, Boschloo, L, Schoevers, RA, Borsboom, D (2017). Mental disorders as networks of problems: a review of recent insights. Social Psychiatry and Psychiatric Epidemiology 52, 1–10.
Fried, EI, van Borkulo, CD, Epskamp, S, Schoevers, RA, Tuerlinckx, F, Borsboom, D (2016 a). Measuring depression over time… or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression. Psychological Assessment 28, 1354–1367.
Goekoop, R, Goekoop, JG (2014). A network view on psychiatric disorders: network clusters of symptoms as elementary syndromes of psychopathology. PLoS ONE 9, e112734.
Granger, CW (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society 37, 424–438.
Grömping, U (2006). Relative importance for linear regression in R: the package relaimpo. Journal of Statistical Software 17, 1–27.
Grömping, U (2007). Estimators of relative importance in linear regression based on variance decomposition. The American Statistician 61, 139–147.
Haslbeck, J, Waldorp, LJ (2015). Structure estimation for mixed graphical models in high-dimensional data. arXiv preprint arXiv:1510.05677.
Haslbeck, J, Waldorp, LJ (2016 a). How well do Network Models predict Future Observations? On the Importance of Predictability in Network Models. arXiv preprint arXiv:1610.09108.
Haslbeck, JMB, Waldorp, LJ (2016 b). mgm: structure estimation for time-varying mixed graphical models in high-dimensional data. arXiv preprint, page. arXiv preprint arXiv:1510.06871.
Hoorelbeke, K, Marchetti, I, De Schryver, M, Koster, EH (2016). The interplay between cognitive risk and resilience factors in remitted depression: a network analysis. Journal of Affective Disorders 195, 96–104.
Koenders, MA, de Kleijn, R, Giltay, EJ, Elzinga, BM, Spinhoven, P, Spijker, AT (2015). A network approach to bipolar symptomatology in patients with different course types. PLoS ONE 10, e0141420.
McNally, RJ, Robinaugh, DJ, Wu, GW, Wang, L, Deserno, MK, Borsboom, D (2015). Mental disorders as causal systems a network approach to posttraumatic stress disorder. Clinical Psychological Science 3, 836–849.
Newman, M, Barabasi, AL, Watts, DJ (2011). The Structure and Dynamics of Networks. Princeton University Press: Princeton, NJ.
Pearl, TVJ (1991). Equivalence and synthesis of causal models. In Proceedings of Sixth Conference on Uncertainty in Artificial Intelligence, Cambridge, MA, pp. 220–227.
Rhemtulla, M, Fried, EI, Aggen, SH, Tuerlinckx, F, Kendler, KS, Borsboom, D (2016). Network analysis of substance abuse and dependence symptoms. Drug and Alcohol Dependence 161, 230–237.
Robinaugh, DJ, LeBlanc, NJ, Vuletich, HA, McNally, RJ (2014). Network analysis of persistent complex bereavement disorder in conjugally bereaved adults. Journal of Abnormal Psychology 123, 510.
Robinaugh, DJ, Millner, AJ, McNally, RJ (2016). Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology 125, 747.
Ruzzano, L, Borsboom, D, Geurts, HM (2015). Repetitive behaviors in autism and obsessive–compulsive disorder: new perspectives from a network analysis. Journal of Autism and Developmental Disorders 45, 192–202.
Santos, H, Fried, EI, Asafu-Adjei, J, Ruiz, RJ (2017). Network structure of perinatal depressive symptoms in Latinas: relationship to stress and reproductive biomarkers. Research in Nursing & Health 9999, 1–11.
Spirtes, P, Glymour, CN, Scheines, R (2000). Causation, Prediction, and Search. MIT Press: New York.
van Borkulo, CD, Borsboom, D, Epskamp, S, Blanken, TF, Boschloo, L, Schoevers, RA, Waldorp, LJ (2014). A new method for constructing networks from binary data. Scientific Reports 4, 5918.
Wigman, JT, de Vos, S, Wichers, M, van Os, J, Bartels-Velthuis, AA (2016). A transdiagnostic network approach to psychosis. Schizophrenia Bulletin 43, 122–132.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Psychological Medicine
  • ISSN: 0033-2917
  • EISSN: 1469-8978
  • URL: /core/journals/psychological-medicine
Please enter your name
Please enter a valid email address
Who would you like to send this to? *

CAPTCHA *

Skip to the audio challenge
×

Keywords:

Type Description Title
UNKNOWN
Supplementary materials

Haslbeck and Fried supplementary material
Haslbeck and Fried supplementary material

 Unknown (2.0 MB)
2.0 MB

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 58
Total number of PDF views: 632 *
Loading metrics...

Abstract views

Total abstract views: 772 *
Loading metrics...

* Views captured on Cambridge Core between 19th June 2017 - 24th April 2018. This data will be updated every 24 hours.

Cancel
Confirm
×