Hostname: page-component-7bb8b95d7b-lvwk9 Total loading time: 0 Render date: 2024-10-02T17:20:47.707Z Has data issue: false hasContentIssue false
  • English
  • Français

Order effects in PTSD network analysis: important implications for diagnostic conceptualization, treatment refinement, and research

Published online by Cambridge University Press:  02 December 2020

Benjamin Trachik Open the ORCID record for Benjamin Trachik [Opens in a new window] ,
Toby D. Elliman ,
Michelle L. Ganulin ,
Michael N. Dretsch ,
Lyndon A. Riviere ,
Oscar A. Cabrera ,
Jeffrey L. Thomas  and
Charles W. Hoge
Benjamin Trachik*
Affiliation:
US Army Medical Research Directorate-West, Walter Reed Army Institute of Research, Joint Base Lewis-McChord, WA 98433, USA
Toby D. Elliman
Affiliation:
Walter Reed Army Institute of Research, Silver Spring, MD, USA
Michelle L. Ganulin
Affiliation:
US Army Medical Research Directorate-West, Walter Reed Army Institute of Research, Joint Base Lewis-McChord, WA 98433, USA
Michael N. Dretsch
Affiliation:
US Army Medical Research Directorate-West, Walter Reed Army Institute of Research, Joint Base Lewis-McChord, WA 98433, USA
Lyndon A. Riviere
Affiliation:
Walter Reed Army Institute of Research, Silver Spring, MD, USA
Oscar A. Cabrera
Affiliation:
US Army Medical Research Directorate-West, Walter Reed Army Institute of Research, Joint Base Lewis-McChord, WA 98433, USA
Jeffrey L. Thomas
Affiliation:
Walter Reed Army Institute of Research, Silver Spring, MD, USA
Charles W. Hoge
Affiliation:
Walter Reed Army Institute of Research, Silver Spring, MD, USA
*
Author for correspondence: Benjamin Trachik, E-mail: Benjamin.j.trachik.mil@mail.mil
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

For decades confirmatory factor analysis (CFA) has been the preeminent method to study the underlying structure of posttraumatic stress disorder (PTSD); however, methodological limitations of CFA have led to the emergence of other analytic approaches. In particular, network analysis has become a gold standard to investigate the structure and relationships between PTSD symptoms. A key methodological limitation, however, which has significant clinical implications, is the lack of data on the potential impact of item order effects on the conclusions reached through network analyses.

Methods

The current study, involving a large sample (N = 5055) of active duty army soldiers following deployment to Iraq, assessed the vulnerability of network analyses and prevalence rate to item order effects. This was done by comparing symptom networks of the DSM-IV PTSD checklist items to these same items distributed in random order. Half of the participants rated their symptoms on traditionally ordered items and half the participants rated the same items, but in random order and interspersed between items from other validated scales. Differences in prevalence rate and network composition were examined.

Results

The prevalence rate differed between the ordered and random item samples. Network analyses using the ordered survey closely replicated the conclusions reached in the existing network analyses literature. However, in the random item survey, network composition differed considerably.

Conclusion

Order effects appear to have a significant impact on conclusions reached from PTSD network analysis. Prevalence rates were also impacted by order effects. These findings have important diagnostic and clinical treatment implications.

Keywords

PTSDnetwork analysisorder effectsdiagnostic criteriaconfirmatory factor analysis
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 13 , October 2022 , pp. 2492 - 2499
Check for updates
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Armour, C., Fried, E. I., Deserno, M. K., Tsai, J., & Pietrzak, R. H. (2017). A network analysis of DSM-5 posttraumatic stress disorder symptoms and correlates in US military veterans. Journal of Anxiety Disorders, 45, 49–59.CrossRefGoogle Scholar
Armour, C., Műllerová, J., & Elhai, J. D. (2016). A systematic literature review of PTSD's latent structure in the diagnostic and statistical manual of mental disorders: DSM-IV to DSM-5. Clinical Psychology Review, 44, 60–74.CrossRefGoogle ScholarPubMed
Armour, C., Tsai, J., Durham, T. A., Charak, R., Biehn, T. L., Elhai, J. D., & Pietrzak, R. H. (2015). Dimensional structure of DSM-5 posttraumatic stress symptoms: support for a hybrid anhedonia and externalizing behaviors model. Journal of Psychiatric Research, 61, 106–113.CrossRefGoogle ScholarPubMed
Asmundson, G. J., Wright, K. D., McCreary, D. R., & Pedlar, D. (2003). Post-traumatic stress disorder symptoms in United Nations peacekeepers: an examination of factor structure in peacekeepers with and without chronic pain. Cognitive Behaviour Therapy, 32(1), 26–37.CrossRefGoogle ScholarPubMed
Bartels, L., Berliner, L., Holt, T., Jensen, T., Jungbluth, N., Plener, P., … Sachser, C. (2019). The importance of the DSM-5 posttraumatic stress disorder symptoms of cognitions and mood in traumatized children and adolescents: Two network approaches. Journal of Child Psychology and Psychiatry, 60(5), 545–554.CrossRefGoogle ScholarPubMed
Beidel, D. C., Frueh, B. C., Neer, S. M., Bowers, C. A., Trachik, B., Uhde, T. W., & Grubaugh, A. (2019). Trauma management therapy with virtual-reality augmented exposure therapy for combat-related PTSD: a randomized controlled trial. Journal of Anxiety Disorders, 61, 64–74.CrossRefGoogle ScholarPubMed
Benfer, N., Bardeen, J. R., Cero, I., Kramer, L. B., Whiteman, S. E., Rogers, T. A., … Weathers, F. W. (2018). Network models of posttraumatic stress symptoms across trauma types. Journal of Anxiety Disorders, 58, 70–77.CrossRefGoogle ScholarPubMed
Borsboom, D., & Cramer, A. O. (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121.CrossRefGoogle Scholar
Boschloo, L., van Borkulo, C. D., Rhemtulla, M., Keyes, K. M., Borsboom, D., & Schoevers, R. A. (2015). The network structure of symptoms of the diagnostic and statistical manual of mental disorders. PLoS One, 10(9), e0137621.CrossRefGoogle Scholar
Böttche, M., Ehring, T., Krüger-Gottschalk, A., Rau, H., Schäfer, I., Schellong, J., … Knaevelsrud, C. (2018). Testing the ICD-11 proposal for complex PTSD in trauma-exposed adults: factor structure and symptom profiles. European Journal of Psychotraumatology, 9(1), 1512264.CrossRefGoogle ScholarPubMed
Briere, J. (2001). Detailed Assessment of Posttraumatic Stress (DAPS). Odessa, FL: Psychological Assessment Resources.Google Scholar
Bryant, R. A., Creamer, M., O'Donnell, M., Forbes, D., McFarlane, A. C., Silove, D., & Hadzi-Pavlovic, D. (2017). Acute and chronic posttraumatic stress symptoms in the emergence of posttraumatic stress disorder: a network analysis. JAMA Psychiatry, 74(2), 135–142.CrossRefGoogle ScholarPubMed
Campbell, S. B., Trachik, B., Goldberg, S., & Simpson, T. L. (2020). Identifying PTSD symptom typologies: a latent class analysis. Psychiatry Research, 285, 112779.CrossRefGoogle ScholarPubMed
Djelantik, A. M. J., Robinaugh, D. J., Kleber, R. J., Smid, G. E., & Boelen, P. A. (2020). Symptomatology following loss and trauma: latent class and network analyses of prolonged grief disorder, posttraumatic stress disorder, and depression in a treatment-seeking trauma-exposed sample. Depression and Anxiety, 37(1), 26–34.CrossRefGoogle Scholar
Elhai, J. D., Contractor, A. A., Tamburrino, M., Fine, T. H., Cohen, G., Shirley, E., … Galea, S. (2015). Structural relations between DSM-5 PTSD and major depression symptoms in military soldiers. Journal of Affective Disorders, 175, 373–378.CrossRefGoogle ScholarPubMed
Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: a tutorial paper. Behavior Research Methods, 50(1), 195–212.CrossRefGoogle ScholarPubMed
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617.CrossRefGoogle ScholarPubMed
Foa, E., Hembree, E., & Rothbaum, B. O. (2007). Prolonged exposure therapy for PTSD: emotional processing of traumatic experiences therapist guide. New York, NY: Oxford University Press.CrossRefGoogle Scholar
Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. Paper presented at the Advances in Neural Information Processing Systems.Google Scholar
Fruchterman, T. M., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21(11), 1129–1164.Google Scholar
Greene, T., Gelkopf, M., Fried, E. I., Robinaugh, D. J., & Lapid Pickman, L. (2020). Dynamic network analysis of negative emotions and DSM-5 posttraumatic stress disorder symptom clusters during conflict. Journal of Traumatic Stress, 33(1), 72–83.CrossRefGoogle ScholarPubMed
Hoffart, A., Langkaas, T. F., Øktedalen, T., & Johnson, S. U. (2019). The temporal dynamics of symptoms during exposure therapies of PTSD: a network approach. European Journal of Psychotraumatology, 10(1), 1618134.CrossRefGoogle ScholarPubMed
Hoge, C. W., Yehuda, R., Castro, C. A., McFarlane, A. C., Vermetten, E., Jetly, R., … Rothbaum, B. O. (2016). Unintended consequences of changing the definition of posttraumatic stress disorder in DSM-5: critique and call for action. JAMA Psychiatry, 73(7), 750–752.CrossRefGoogle ScholarPubMed
Horn, S. R., Pietrzak, R. H., Schechter, C., Bromet, E. J., Katz, C. L., Reissman, D. B., … Herbert, R. (2016). Latent typologies of posttraumatic stress disorder in World Trade Center responders. Journal of Psychiatric Research, 83, 151–159.CrossRefGoogle ScholarPubMed
Marshall, G. N., Schell, T. L., & Miles, J. N. (2013). A multi-sample confirmatory factor analysis of PTSD symptoms: what exactly is wrong with the DSM-IV structure? Clinical Psychology Review, 33(1), 54–66.CrossRefGoogle ScholarPubMed
McBride, O., Hyland, P., Murphy, J., & Elklit, A. (2020). Network analysis of posttraumatic stress experiences of adults seeking psychological treatment for childhood sexual abuse. Journal of Traumatic Stress, 33(1), 10–18.CrossRefGoogle ScholarPubMed
McNally, R. J. (2016). Can network analysis transform psychopathology? Behaviour Research and Therapy, 86, 95–104.CrossRefGoogle ScholarPubMed
O'Donnell, M. L., Alkemade, N., Nickerson, A., Creamer, M., McFarlane, A. C., Silove, D., … Forbes, D. (2014). Impact of the diagnostic changes to post-traumatic stress disorder for DSM-5 and the proposed changes to ICD-11. British Journal of Psychiatry, 205(3), 230–235.CrossRefGoogle ScholarPubMed
Papini, S., Rubin, M., Telch, M. J., Smits, J. A., & Hien, D. A. (2020). Pretreatment posttraumatic stress disorder symptom network metrics predict the strength of the association between node change and network change during treatment. Journal of Traumatic Stress, 33(1), 64–71.CrossRefGoogle ScholarPubMed
Phillips, R. D., Wilson, S. M., Sun, D., VA Mid-Atlantic MIRECC, Workgroup, Morey, R., Van Voorhees, E., … Tupler, L. A. (2018). Posttraumatic stress disorder symptom network analysis in US military veterans: examining the impact of combat exposure. Frontiers in Psychiatry, 9, 608.CrossRefGoogle Scholar
Pietrzak, R. H., el-Gabalawy, R., Tsai, J., Sareen, J., Neumeister, A., & Southwick, S. M. (2014). Typologies of posttraumatic stress disorder in the U.S. adult population. Journal of Affective Disorders, 162, 102–106.CrossRefGoogle ScholarPubMed
Price, M., Legrand, A. C., Brier, Z. M., & Hébert-Dufresne, L. (2019). The symptoms at the center: examining the comorbidity of posttraumatic stress disorder, generalized anxiety disorder, and depression with network analysis. Journal of Psychiatric Research, 109, 52–58.CrossRefGoogle Scholar
Price, M., & van Stolk-Cooke, K. (2015). Examination of the interrelations between the factors of PTSD, major depression, and generalized anxiety disorder in a heterogeneous trauma-exposed sample using DSM 5 criteria. Journal of Affective Disorders, 186, 149–155.CrossRefGoogle Scholar
Resick, P. A., & Miller, M. W. (2009). Posttraumatic stress disorder: anxiety or traumatic stress disorder? Journal of Traumatic Stress, 22(5), 384–390.CrossRefGoogle ScholarPubMed
Richardson, L. K., Frueh, B. C., & Acierno, R. (2010). Prevalence estimates of combat-related post-traumatic stress disorder: critical review. Australian and New Zealand Journal of Psychiatry, 44(1), 4–19.CrossRefGoogle ScholarPubMed
Ross, J., Murphy, D., & Armour, C. (2018). A network analysis of DSM-5 posttraumatic stress disorder and functional impairment in UK treatment-seeking veterans. Journal of Anxiety Disorders, 57, 7–15.CrossRefGoogle ScholarPubMed
Rubin, D. C., Berntsen, D., & Bohni, M. K. (2008). A memory-based model of posttraumatic stress disorder: evaluating basic assumptions underlying the PTSD diagnosis. Psychological Review, 115(4), 985.CrossRefGoogle ScholarPubMed
Segal, A., Wald, I., Lubin, G., Fruchter, E., Ginat, K., Yehuda, A. B., … Bar-Haim, Y. (2019). Changes in the dynamic network structure of PTSD symptoms pre-to-post combat. Psychological Medicine, 1–8.Google ScholarPubMed
von Stockert, S. H., Fried, E. I., Armour, C., & Pietrzak, R. H. (2018). Evaluating the stability of DSM-5 PTSD symptom network structure in a national sample of US military veterans. Journal of Affective Disorders, 229, 63–68.CrossRefGoogle Scholar
Weathers, F. W., Litz, B. T., Herman, D. S., Huska, J. A., & Keane, T. M. (1993). The PTSD checklist (PCL): reliability, validity, and diagnostic utility. Paper presented at the Annual Convention of the International Society for Traumatic Stress Studies, San Antonio, TX.Google Scholar
Witte, T. K., Domino, J. L., & Weathers, F. W. (2015). Item order effects in the evaluation of posttraumatic stress disorder symptom structure. Psychological Assessment, 27(3), 852.CrossRefGoogle ScholarPubMed
Supplementary material: File

Trachik et al. supplementary material

Trachik et al. supplementary material

Download Trachik et al. supplementary material(File)
File 1.2 MB