Courses

Although researchers do their best to avoid missing data, it is a common problem in medical and epidemiological studies. How large the impact is of missing data on the study results and how to solve the missing data problem depends on how much data is missing and why the data are missing. This three-day course provides you with simple and advanced tools how to evaluate and handle missing data i…

The purpose of a prediction model is to estimate the probability of the presence of a particular outcome as accurately as possible. Prediction models are often developed with clinical practice in mind, and involve combining information about individual patients to calculate an individual’s probability of illness or recovery. The model can then be presented in the form of a clinical predictive r…

In this course students gain an overview on the statistical techniques and research designs used by epidemiologists to estimate treatment effects from patient data and learn how to apply these techniques. We begin by introducing the Neyman-Rubin causal model (RCM), also called the potential outcomes framework, which postulates that each patient has as many potential outcomes as there are treatm…

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 inc…

Effective communication of scientific data is one of the most important skills of a scientist. Stated bluntly, if the intended audience does not get the message in the data (and acts on it), the research effort is wasted. Data visualization in tables and graphs is essential to effective communication, because it can convey complex relationships in a way unmatched by simple text. However, data v…

Multilevel models, also known as mixed models are used to analyse correlated observations. Correlated observations can occur, for instance, when subjects are clustered within neighbourhoods, patients are clustered within hospitals, students are clustered within schools, etc. Besides this, correlated observations also occur in longitudinal studies where the repeated measurements over time are cl…

This 12 ECTS microcredential introduces students to the basic principles of epidemiology. The course begins with an overview of the history and recent and new developments in epidemiology. It then delves into measures of occurrence and association, and the concepts of causality and systematic error. Finally, the main research designs used in epidemiology are discussed. In particular, the genera…

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: concepts in infectious diseases epidemiology, surveillance, outbreak management, prevention of infectious diseases, role of the Public Health Service Epidemiology of infectious diseases is a separate branch within epidemiology and requires a broad view and working in multidisciplinary teams. Using the appropriate epidemiological methods is crucial in fighting infectious disease…