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.

From our analyses, it appears there are “degrees” of prior settings that produce viable results, rather than just one setting of priors giving “good” results. For example, one of the criticisms that may arise regarding our approach is that one could argue that the priors are doing all of the work, especially in the presence of small groups (or classes). Because of the priors resembling a plausible parameter space, we are increasing the power to detect a small group in a dataset rather than completely constructing the group based on highly informative priors.