Courses

Topics covered: measures of disease frequency and association, standardization and misclassification, observational study designs, confounding and effect modification, causal reasoning and diagrams By means of lectures, exercises and skills training sessions the following topics in observational research will be covered extensively: observational research designs and their advantages and disad…

The course covers statistical methods to be used in the situation where one or more outcome variables are repeatedly measured in time on the same experimental unit. For instance, in a clinical trial, the outcome variable can be measured at baseline and at different times during the treatment period. For this type of data, traditional regression models cannot be used since outcomes of the same s…

Topics covered: statistical methods, machine-learning methods, developing prediction models, validation of prediction models, impact of prediction models (basic knowledge of R required) Clinical prediction models are modern tools that provide the likely diagnosis or prognosis of an individual patient based on their data. This supports patients and doctors when they have to make evidence-based …

Topics covered: overlap between epidemiology and data science, data exploration and visualization, data simplification and dimensionality reduction, creating understandable code (basic knowledge of R required) Epidemiology and data science fields overlap considerably, since both fields employ quantitative methods to get actionable clinical insight from patient data. A data scientist’s skill is…

Topics covered: biomarkers, study designs in molecular epidemiology, genetic epidemiology, mendelian randomization, big data / omics Biomarker profiles that can be used in molecular epidemiology can range from just a few targeted markers to a whole metabolome, and may include the measurement of (epi)genetic variation, gene expression, proteins, small molecules, and functional assays. Epidemiol…