This module provides an introduction to Bayesian methods for biomedical research. Specifically, we discuss the Bayesian paradigm introducing the subjective interpretation of probability; Bayes Theorem; and prior, posterior and predictive distributions. We contrast the Bayesian and frequentist approaches using simple biomedical problems including diagnostic testing and design and monitoring of clinical trials, among others.
This module uses INLA and a number of R packages to illustrate the application of Bayesian methods to analyze independent data.
Pre-requisite: introductory course in statistics/biostatistics; linear regression; familiarity with R/RStudio.