title: An introduction to using Bayesian linear regression with clinical data.
abstract: Statistical training psychology focuses on frequentist methods. Bayesian methods are an alternative to standard frequentist methods. This article provides researchers with an introduction to fundamental ideas in Bayesian modeling. We use data from an electroencephalogram (EEG) and anxiety study to illustrate Bayesian models. Specifically, the models examine the relationship between error-related negativity (ERN), a particular event-related potential, and trait anxiety. Methodological topics covered include: how to set up a regression model in a Bayesian framework, specifying priors, examining convergence of the model, visualizing and interpreting posterior distributions, interval estimates, expected and predicted values, and model comparison tools. We also discuss situations where Bayesian methods can outperform frequentist methods as well has how to specify more complicated regression models. Finally, we conclude with recommendations about reporting guidelines for those using Bayesian methods in their own research. We provide data and R code for replicating our analyses.
authors.affiliation: Department of Psychology, Brigham Young University, USA. Electronic address: scott_baldwin@byu.edu.
authors.forename: Scott A
authors.initials: SA
authors.lastname: Baldwin
authors.affiliation: Department of Psychology and Neuroscience Center, Brigham Young University, USA.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Larson
mesh_terms: D001007:Anxiety / Q000503:physiopathology; D001499:Bayes Theorem; D000068456:Clinical Studies as Topic / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology; D006801:Humans; D016014:Linear Models
keywords: Bayesian methods; Error-related negativity (ERN); Event-related potential; MCMC; Prediction; R; Stan