In health sciences, researchers are typically interested in estimating population parameters, such as the mean, difference of means, proportions, differences in proportions, etc. When using classical frequentist statistics, these parameters are estimated using data from one particular study. Although there is often a priori knowledge about likely values of a parameter, this knowledge is not included in the analysis of the current study. Central to Bayesian statistics is the idea that a ‘before’, a-priori, estimate of the probable value of a parameter is revised to an ‘after’, a posteriori, estimate based on new data. This idea fits in well with the way of thinking in medical decision-making. The Bayesian method offers the possibility to combine various data sources to update what is already known, while making inference about the uncertainty of the updated knowledge.
This 3-day course introduces the basics of Bayesian statistics and Bayesian thinking. The student will learn how to perform a Bayesian analysis of a proportion, a mean, and simple multiparameter models. In addition, the student will be introduced to Markov Chain Monte Carlo sampling and will gain understanding of real-world problems where the Bayesian approach is particularly useful. Special attention will be given to the interpretation of the results of a Bayesian analysis. The course consists of lectures and computer practicals. During the lectures, the application of Bayesian analysis is illustrated with examples from medical and epidemiological practice. During the computer practicals, the students will use the computer program R to carry out Bayesian analyses.
The morning programme consists of lectures and the afternoon programme of computer practicals.
