4,689
Views
35
CrossRef citations to date
0
Altmetric
Tutorial

Bayesian PTSD-Trajectory Analysis with Informed Priors Based on a Systematic Literature Search and Expert Elicitation

ORCID Icon, , ORCID Icon, ORCID Icon, &

References

  • American Psychiatric Association (2013). Diagnostic and statistical manual of the mental Disorders (5th ed.). Washington, DC: APA.
  • Andrews, B., Brewin, C. R., Philpott, R., & Stewart, L. (2007). Delayed-onset posttraumatic stress disorder: A systematic review of the evidence. American Journal of Psychiatry, 164, 1319–26. DOI: 10.1176/appi.ajp.2007.06091491
  • Ashby, D. (2006). Bayesian statistics in medicine: A 25 year review. Statistics in Medicine, 25(21), 3589–3631.
  • Bakker, A., Van der Heijden, P. G., Van Son, M. J., & Van Loey, N. E. (2013). Course of traumatic stress reactions in couples after a burn event to their young child. Health Psychology, 32(10), 1076–1083.
  • Baldwin, S. A., & Fellingham, G. W. (2013). Bayesian methods for the analysis of small sample multilevel data with a complex variance structure. Psychological Methods, 18(2), 151–164.
  • Bauer, D. J., & Curran, P. J. (2003). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods, 8(3), 338–363.
  • Bonanno, G. A. (2004). Loss, trauma, and human resilience: Have we underestimated the human capacity to thrive after extremely aversive events? American Psychologist, 59(1), 20–28.
  • Bonanno, G. A., Kennedy, P., Galatzer-Levy, I., Lude, P., & Elfström, M. L. (2012). Trajectories of resilience, depression, and anxiety following spinal cord injury. Rehabilitation Psychology, 57, 236–247. DOI: 10.1037/a0029256.
  • Bonanno, G. A., Mancini, A. D., Horton, J. L., Powell, T. M., LeardMann, C. A., Boyko, E. J., … Smith, T. C. (2012). Trajectories of trauma symptoms and resilience in deployed US military service members: Prospective cohort study. British Journal of Psychiatry, 200(4), 317–323.
  • Breslau, N., & Davis, G. C. (1992). Posttraumatic stress disorder in an urban population of young adults: Risk factors for chronicity. The American Journal of Psychiatry, 149(5), 671–675.
  • Brom, D., & Kleber, R. J. (1985). De Schok Verwerkings Lijst. Nederlands Tijdschrift voor de Psychologie, 40, 164–168.
  • De la Cruz-Mesía, R., Quintana, F. A., & Marshall, G. (2008). Model-based clustering for longitudinal data. Computational Statistics & Data Analysis, 52(3), 1441–1457.
  • de Vries, G. J., & Olff, M. (2009). The lifetime prevalence of traumatic events and posttraumatic stress disorder in the Netherlands. Journal of Traumatic Stress, 22(4), 259–267.
  • Depaoli, S. (2013). Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation. Psychological Methods, 18(2), 186–219.
  • Depaoli, S., & Boyajian, J. (2014). Linear and nonlinear growth models: Describing a bayesian perspective. Journal of Consulting and Clinical Psychology, 82(5), 784–802.
  • Depaoli, S., & van de Schoot, R. (2017). Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist. Psychological Methods, 22(2), 240–261.
  • Depaoli, S., van de Schoot, R., van Loey, N., & Sijbrandij, M. (2015). Using Bayesian statistics for modeling PTSD through Latent Growth Mixture Modeling: Implementation and discussion. European Journal of Psychotraumatology, 6, 27516. https://doi.org/10.3402/ejpt.v6.27516.
  • Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2004). Bayesian data analysis (Vol. 2). London: Chapman&HallCRC.
  • Henson, J. M., Reise, S. P., & Kim, K. H. (2007). Detecting mixtures from structural model differences using latent variable mixture modeling: A comparison of relative model fit statistics. Structural Equation Modeling, 14(2), 202–226.
  • Horowitz, M., Wilner, N., & Alvarez, W. (1979). Impact of Event Scale: A measure of subjective stress. Psychosomatic medicine, 41(3), 209–218. http://dx.doi.org/10.1097/00006842-197905000-00004.
  • Hox, J., van de Schoot, R., & Matthijsse, S. (2012). How few countries will do? Comparative survey analysis from a Bayesian perspective. Survey Research Methods, 6(2), 87–93.
  • Kaplan, D. (2014). Bayesian statistics for the social sciences. New-York: Guilford Publications.
  • Kaplan, D., & Depaoli, S. (2012). Bayesian structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 650–673). New York: Guilford.
  • Kohli, N., Hughes, J., Wang, C., Zopluoglu, C., & Davison, M. L. (2015). Fitting a linear-linear piecewise growth mixture model with unknown knots: A comparison of two common approaches to inference. Psychological Methods, 20(2), 259–275.
  • König, C., & van de Schoot, R. (2017). Bayesian statistics in educational research: A look at the current state of affairs. Educational Review, 1–24. doi: 10.1080/00131911.2017.1350636.
  • Kruschke, J. K. (2010). Bayesian data analysis. Wiley Interdisciplinary Reviews: Cognitive Science, 1(5), 658–676.
  • Kruschke, J. K. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. London: Academic Press.
  • Lenk, P. J., & Desarbo, W. S. (2000). Bayesian inference for finite mixtures of generalized linear models with random effects. Psychometrika, 65(1), 93–119.
  • Lubke, G., & Neale, M. (2008). Distinguishing between latent classes and continuous factors with categorical outcomes: Class invariance of parameters of factor mixture models. Multivariate Behavioral Research, 43(4), 592–620.
  • Mouthaan, J., Sijbrandij, M., De Vries, G. J., Reitsma, J. B., Van de Schoot, R., Goslings, J. C., … Olff, M. (2013). Internet-based early intervention to prevent posttraumatic stress disorder in injury patients: Randomized controlled trial. Journal of Medical Internet Research, 15(8), e165.
  • Muthén, B. O., & Muthén, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882–891.
  • Muthén, L. K., & Muthén, B. O. (1998–2015). Mplus user's guide (7th ed.). Los Angeles, CA, USA.
  • Nylund, K., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569.
  • Pietrzak, R. H., Van Ness, P. H., Fried, T. R., Galea, S., & Norris, F. H. (2013). Trajectories of posttraumatic stress symptomatology in older persons affected by a large-magnitude disaster. Journal of Psychiatric Research, 47(4), 520–526.
  • Rietbergen, C., Debray, T. P., Klugkist, I., Janssen, K. J., & Moons, K. G. (2017). Reporting of Bayesian analysis in epidemiologic research should become more transparent. Journal of Clinical Epidemiology, 86, 51–58.
  • Rouder, J. N., Sun, D., Speckman, P. L., Lu, J., & Zhou, D. (2003). A hierarchical Bayesian statistical framework for response time distributions. Psychometrika, 68(4), 589–606.
  • Rupp, A. A., Dey, D. K., & Zumbo, B. D. (2004). To Bayes or not to Bayes, from whether to when: Applications of Bayesian methodology to modeling. Structural Equation Modeling, 11(3), 424–451.
  • Shalev, A. Y., Peri, T., Canetti, L., & Schreiber, S. (1996). Predictors of PTSD in injured trauma survivors: A prospective study. American Journal of Psychiatry, 153(2), 219–225.
  • Smid, G. E., Mooren, T. T., van der Mast, R. C., Gersons, B. P., & Kleber, R. J. (2009). Delayed posttraumatic stress disorder: Systematic review, meta-analysis, and meta-regression analysis of prospective studies. Journal of Clinical Psychiatry, 70(11), 1572–1582.
  • Smid, G. E., Van Der Velden, P. G., Gersons, B. P. R., & Kleber, R. J. (2012). Late-onset posttraumatic stress disorder following a disaster: A longitudinal study. Psychological Trauma: Theory, Research, Practice, and Policy, 4(3), 312–322.
  • Southwick, S. M., Bonanno, G. A., Masten, A. S., Panter-Brick, C., & Yehuda, R. (2014). Resilience definitions, theory, and challenges: Interdisciplinary perspectives. European Journal of Psychotraumatoly, 5(1), 1–14.
  • Spiegelhalter, D. J., Myles, J., Jones, D., & Abrams, K. (2000). Bayesian methods in health technology assessment: A review. Health Technology Assessment, 4(38), 1–130.
  • Sveen, J., Low, A., Dyster-Aas, J., Ekselius, L., Willebrand, M., & Gerdin, B. (2010). Validation of a Swedish version of the Impact of Event Scale-Revised (IES-R) in patients with burns. Journal of Anxiety Disorders, 24(6), 618–622.
  • Ter Smitten, M. H., de Graaf, R., & Van Loey, N. E. (2011). Prevalence and co-morbidity of psychiatric disorders 1–4 years after burn. Burns, 37(5), 753–761.
  • Thordardottir, E. B., Valdimarsdottir, U. A., Hansdottir, I., Hauksdottir, A., Dyregrov, A., Shipherd, J. C., … Gudmundsdottir, B. (2016). Sixteen-year follow-up of childhood avalanche survivors. European Journal of Psychotraumatoly, 7(1), 1–9.
  • Thormar, S. B., Sijbrandij, M., Gersons, B. P., Van de Schoot, R., Juen, B., Karlsson, T., … Olff, M. (2016). PTSD Symptom trajectories in disaster volunteers: The role of Self-efficacy, social acknowledgement, and tasks carried out. Journal of Traumatic Stress, 29(1), 17–25.
  • Tofighi, D., & Enders, C. K. (2008). Identifying the correct number of classes in growth mixture models. In G. R. Hancock (Ed.), Mixture models in latent variable research (pp. 317–341). Greenwich, CT: Information Age.
  • Van de Schoot, R., Broere, J. J., Perryck, K. H., Zondervan-Zwijnenburg, M., & Van Loey, N. E. (2015). Analyzing small data sets using Bayesian estimation: The case of posttraumatic stress symptoms following mechanical ventilation in burn survivors. European Journal of Psychotraumatology, 6, 1–14. https://doi.org/10.3402/ejpt.v6.25216.
  • Van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., & van Aken, M. A. G. (2014). A gentle introduction to bayesian analysis: Applications to developmental research. Child Development, 85(3), 842–860.
  • van de Schoot, R., Schalken, N., & Olff, M. (2017). Systematic search of Bayesian statistics in the field of Psychotraumatology. European Journal of Psychotraumatology, 18(sup1), 1–6. https://doi.org/10.1080/20008198.2017.1375339.
  • Van De Schoot, R., Sijbrandij, M., Winter, S. D., Depaoli, S., & Vermunt, J. K. (2017). The GRoLTS-Checklist: Guidelines for reporting on latent trajectory studies. Structural Equation Modeling: A Multidisciplinary Journal, 24(3), 451–467.
  • van de Schoot, R., Winter, S. D., Ryan, O., Zondervan-Zwijnenburg, M., & Depaoli, S. (2017). A systematic review of Bayesian articles in psychology: The last 25 years. Psychological Methods, 22(2), 217–239.
  • Van Loey, N. E., Maas, C. J., Faber, A. W., & Taal, L. A. (2003). Predictors of chronic posttraumatic stress symptoms following burn injury: Results of a longitudinal study. Journal of Traumatic Stress, 16(4), 361–369.
  • Van Loey, N. E., van de Schoot, R., & Faber, A. W. (2012). Posttraumatic stress symptoms after exposure to two fire disasters: Comparative study. PLoS ONE, 7(7), e41532.
  • Vermunt, J. K. (2010). Longitudinal research using mixture models Longitudinal research with latent variables (pp. 119–152). Berlin: Springer.
  • Yang, R., & Berger, J. (1998). A catalog of noninformative priors. Institute of Statistics and Decision Sciences, Retrieved from https://yaroslavvb.com/papers/yang-catalog.pdf.