During this course, you will learn to use statistical methods to study the association between (multiple) determinants and the occurrence of an outcome event. The course will begin with an introduction to likelihood theory, using simple examples and a minimum of mathematics. You will then move to learning about the most important regression models used in medical research. These include logistic regression, Poisson regression, analysis of ‘event history’ data, and the Cox proportional hazards regression model. In addition, you will become familiar with model validation and regression diagnostics, as well as with the basic principles of resampling methods and longitudinal data analysis.
By the end of the course, you should be able to:
- Explain the principles of the likelihood theory and maximum likelihood methods
- Explain the principles of the following statistical analysis techniques: Logistic regression analysis, Poisson regression analysis, Analysis of event history data, including the Cox proportional hazards regression model
- Explain model validation and regression diagnostics
- Describe the basic principles of longitudinal data analysis
- Apply the above-mentioned techniques using common statistical packages (e.g. SPSS or R)
- Name the situations in which these techniques can be applied and the conditions that should be met to obtain reliable results using these techniques
- Explain and interpret the results obtained with these techniques, and apply these results in practice (e.g. to answer a research question)
Cas Kruitwagen MSc
Cas Kruitwagen is lecturer of the core courses Introduction to Statistics, Classical Methods in Data Anlaysis and Modern Methods in Data Analysis, and for specialization course Evidence-Based Assessment of New Imaging Techniques.
More information about Cas Kruitwagen can be found here
Jan van den Broek PhD
Jan van den Broek PhD is lecturer of the course Modern Methods in Data Analysis.
More information Dr. J. (Jan) van den Broek can be found here