Bayes with Informed Priors Based on Literature and Expert Elicitation
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).