Bayesian Data Analysis is a powerful tool for incorporating data and prior information into complex and flexible models.

It is well-suited to analyze Randomized Control Trials, where effects are small and data has hierarchical structures.
In this paper, we summarize the approach and methods of Bayesian Data Analysis, and demonstrate how they can be applied to a Randomized Control Trial measuring the effect of a smartphone application on binge eating behavior.
We fit a hierarchical Poisson model, which accounts for individual level and time level effects, allowing us to model treatment effects over time. Our analysis suggests that the smartphone application may cause a small reduction in the number of Objective Bulimic Episodes in some stages.
Bayesian methods allow us to account for the structure of the data, obtain heterogenous treatment effects for different stages of the therapy, and incorporate uncertainty in our parameter estimates. We conclude that Bayesian methods have the potential to improve analyses of Randomized Control Trials in many fields.