文章基本信息

标题：Using Bayesian statistics for modeling PTSD through Latent Growth Mixture Modeling: implementation and discussion
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作者：Sarah Depaoli ; Rens van de Schoot ; Nancy van Loey 等
期刊名称：European Journal of Psychotraumatology
印刷版ISSN：2000-8198
电子版ISSN：2000-8066
出版年度：2015
卷号：6
DOI：10.3402/ejpt.v6.27516%40zept20.2015.6.issue-s2
语种：English
出版社：Taylor & Francis Group
摘要：BackgroundAfter traumatic events, such as disaster, war trauma, and injuries including burns (which is the focus here), the risk to develop posttraumatic stress disorder (PTSD) is approximately 10% (Breslau & Davis, 1992 Breslau N., Davis G. C. Posttraumatic stress disorder in an urban population of young adults: Risk factors for chronicity. The American Journal of Psychiatry. 1992; 149: 671–675. [Google Scholar]). Latent Growth Mixture Modeling can be used to classify individuals into distinct groups exhibiting different patterns of PTSD (Galatzer-Levy, 2015 Galatzer-Levy I. Applications of Latent Growth Mixture Modeling and allied methods to posttraumatic stress response data. European Journal of Psychotraumatology. 2015; 6: 27515. http://dx.doi.org/10.3402/ejpt.v6.27515. [Google Scholar]). Currently, empirical evidence points to four distinct trajectories of PTSD patterns in those who have experienced burn trauma. These trajectories are labeled as: resilient, recovery, chronic, and delayed onset trajectories (e.g., Bonanno, 2004 Bonanno G. A. Loss, trauma, and human resilience: Have we underestimated the human capacity to thrive after extremely adverse events?. American Psychologist. 2004; 59: 20–28. [Google Scholar]; Bonanno, Brewin, Kaniasty, & Greca, 2010 Bonanno G. A., Brewin C. R., Kaniasty K., Greca A. M. L. Weighing the costs of disaster consequences, risks, and resilience in individuals, families, and communities. Psychological Science in the Public Interest. 2010; 11: 1–49. [Google Scholar]; Maercker, Gäbler, O'Neil, Schützwohl, & Müller, 2013 Maercker A., Gäbler I., O'Neil J., Schützwohl M., Müller M. Long-term trajectories of PTSD or resilience in former East German political prisoners. Torture. 2013; 23: 15–27. [PubMed ]. [Google Scholar]; Pietrzak et al., 2013 Pietrzak R. H., Feder A., Singh R., Schechter C. B., Bromet E. J., Katz C. L., etal. Trajectories of PTSD risk and resilience in World Trade Center responders: An 8-year prospective cohort study. Psychological Medicine. 2013; 3: 1–15. [Google Scholar]). The delayed onset trajectory affects only a small group of individuals, that is, about 4–5% (O'Donnell, Elliott, Lau, & Creamer, 2007 O'Donnell M. L., Elliott P., Lau W., Creamer M. PTSD symptom trajectories: From early to chronic response. Behaviour Research and Therapy. 2007; 45: 601–606. [Google Scholar]). In addition to its low frequency, the later onset of this trajectory may contribute to the fact that these individuals can be easily overlooked by professionals. In this special symposium on Estimating PTSD trajectories (Van de Schoot, 2015a Van de Schoot R. Latent Growth Mixture Models to estimate PTSD trajectories. European Journal of Psychotraumatology. 2015a; 6: 27503. http://dx.doi.org/10.3402/ejpt.v6.27503. [Google Scholar]), we illustrate how to properly identify this small group of individuals through the Bayesian estimation framework using previous knowledge through priors (see, e.g., Depaoli & Boyajian, 2014 Depaoli S., Boyajian J. Linear and nonlinear growth models: Describing a Bayesian perspective. Journal of Consulting and Clinical Psychology. 2014; 82(5): 784–802. [Google Scholar]; Van de Schoot, Broere, Perryck, Zondervan-Zwijnenburg, & Van Loey, 2015 Van de Schoot R., Broere J. J., Perryck K., Zondervan-Zwijnenburg M., Van Loey N. Analyzing small data sets using Bayesian estimation: The case of posttraumatic stress symptoms following mechanical ventilation in burn survivors. European Journal of Psychotraumatology. 2015; 6: 25216. http://dx.doi.org/10.3402/ejpt.v6.25216. [Google Scholar]).MethodWe used latent growth mixture modeling (LGMM) (Van de Schoot, 2015b Van de Schoot R. Latent trajectory studies: The basics, how to interpret the results, and what to report. European Journal of Psychotraumatology. 2015b; 6: 27514. http://dx.doi.org/10.3402/ejpt.v6.27514. [Google Scholar]) to estimate PTSD trajectories across 4 years that followed a traumatic burn. We demonstrate and compare results from traditional (maximum likelihood) and Bayesian estimation using priors (see, Depaoli, 2012 Depaoli S. Measurement and structural model class separation in mixture CFA: ML/EM versus MCMC. Structural Equation Modeling: A Multidisciplinary Journal. 2012; 19(2): 178–203.[Taylor & Francis Online] [Google Scholar], 2013 Depaoli S. Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation. Psychological Methods. 2013; 18(2): 186–219. [Google Scholar]). Further, we discuss where priors come from and how to define them in the estimation process.ResultsWe demonstrate that only the Bayesian approach results in the desired theory-driven solution of PTSD trajectories. Since the priors are chosen subjectively, we also present a sensitivity analysis of the Bayesian results to illustrate how to check the impact of the prior knowledge integrated into the model.ConclusionsWe conclude with recommendations and guidelines for researchers looking to implement theory-driven LGMM, and we tailor this discussion to the context of PTSD research.
关键词：Latent Growth Mixture Modeling; Bayesian estimation; sensitivity analysis; PTSD; delayed onset