Category: Core Project

Bayesian PTSD-Trajectory Analysis with Informed Priors

we illustrate how to obtain background information using previous literature in the field of PTSD 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 how to run a Bayesian LGMM.

Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist

Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. Although it is very attractive to use Bayesian statistics, our personal experience has led us to believe that naively applying Bayesian methods can be dangerous for at least 3 main reasons:

The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies

Estimating models within the mixture model framework, like latent growth mixture modeling (LGMM) or latent class growth analysis (LCGA), involves making various decisions throughout the estimation process. This has led to a wide variety in how results of latent trajectory analysis are reported.