There is a recent increase in interest of Bayesian analysis, see for example our systematic searches in the fields of psychology, educational research and the field of psychotraumotology and PTSD.

However, little effort has been made thus far to directly incorporate background knowledge via the prior distribution into the analyses. This process might be especially useful in the context of latent growth mixture modeling when one or more of the latent groups are expected to be relatively small due to what we refer to as limited data. We argue that the use of Bayesian statistics has great advantages in limited data situations, but only if background knowledge can be incorporated into the analysis via prior distributions.

We highlight these advantages through a dataset including patients with burn injuries and analyze trajectories of posttraumatic stress symptoms using the Bayesian framework following the steps of the WAMBS-checklist.

In the included example, we illustrate how to obtain background information using previous literature based on a systematic literature search and by using expert knowledge. Finally, we show how to translate this knowledge into prior distributions and we illustrate the importance of conducting a prior sensitivity analysis.

Van de Schoot, R., Sijbrandij, M., Depaoli, S., Winter, S.D., Olff, M. & Van Loey, N.E. (2018). Bayesian PTSD-Trajectory Analysis with Informed Priors Based on a Systematic Literature Search and Expert Elicitation. Multivariate Behavioral Research, 1-25. DOI: 10.1080/00273171.2017.1412293

We put all the relevant information needed to replicate our findings, including the systematic review data, all the R-scripts, Mplus code and, logbooks, the example data, and much more, on the Open Science Framework (OSF; see https://osf.io/vk4be).

Former team member

After working with Rens on various research projects related to Bayesian Estimation and latent growth modeling I developed an interest in researching both of these further.

Marit Sijbrandij
Associate Professor VU University
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Sarah Depaoli
Assistant Professor at the University of California, Merced
Sarah’s research interests are largely focused on issues surrounding Bayesian estimation of latent variable models. She has a particular interest in estimation issues arising from nonlinear growth patterns over time. She is also interested in improving accuracy of uncovering unobserved (latent) groups of individuals. She is currently working with several students that are involved in research spanning a wide range of methodological topics .
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Miranda Olff
Professor Neurobiological Mechanisms of Treatment in Trauma
Miranda focuses on the psychological and biological responses to traumatic stress. Research includes randomized controlled trials on the effects of debriefing of early trauma interventions, and psychotherapy and pharmacotherapy.
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Nancy van Loey
Program leader Psychosocial & behavioral research (ADBC)
Nancy is related to the departement of Clinical and Health Psychology and program leader at Association of Dutch Burn Centres.
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