Biostatistics (knowledge of various regression models: linear regression, logistic/probit regression, survival models, and if possible hierarchical models). Familiarity with R is also required.

Course Syllabus

Will be published shortly.

Course Description

In the last two decades, the Bayesian approach has become increasingly popular in virtually all application areas. The approach is especially known for its capability to tackle complex statistical modeling tasks. The aim of this course is to introduce the participants smoothly into Bayesian statistical methods, from basic concepts to hierarchical models, model building and model testing. Numerous biostatistical examples (e.g. meta-analyses, longitudinal studies including growth curve modeling, analysis of clinical trials, etc) will illustrate the theoretical concepts. The course is scheduled into classroom teaching and computer exercises, and uses the software packages WinBUGS and OpenBUGS but also their interfaces with R, making use of R2WinBUGS and R2OpenBUGS. The course is based on a recently published Wiley book of Lesaffre and Lawson, entitled Bayesian Biostatistics.

Requirements

To receive academic credit for the course, participants must pass the final exam with a grade of at least 60 (D). Non-credit participants are not required to take the final exam.