Courses

The course discusses the objectives and methodological foundations of a number of commonly used regression methodologies in biomedical research from a practical perspective. In short: what is regression (in terms of assumptions made on data), what is it for (which objectives), when can we use it (or not), how can we interpret results from it (and when not).

Survival Analysis (Advanced Biostatistics) Interested in the study of the distribution of life times, i.e. the times from an initiating event (birth, diagnosis, start of treatment) to some terminal event (relapse, death)? Sign up for the course ‘Survival Analysis’. For more information and the option to register, please visit the website of LUMC Boerhaave Nascholing.

This course is focused on the dynamic nature of infectious diseases, which clearly distinguishes infectious disease epidemiology from the epidemiology of other diseases, such as cardiovascular disease and cancer. In addition to the dynamics of specific infections such as HIV, dynamics of genotypes and genes (such as those encoding antimicrobial resistance) and principles of population biology w…

Learn the basics of machine learning, with a special focus on sparse data as they occur in high dimensional ‘omics’ types of data. At the end of the course, the student will be familiar with and has practical experience with the main methods of machine learning: o Nearest neighbors o Bayes classifiers and discriminant analyses o Decision trees, boosting and random forest o Regulari…

Even in well designed and conducted epidemiological studies, data will be missing. This may include missing observations of the exposure under study, of confounders, or of the outcome. Possible mechanisms for data being missing will be discussed, as well as their potential impact in terms of bias. Focus will be on methods to handle missing data. Examples and exercises will come from various ep…

Survival data, or more generally, time-to-event data (where the “event” can be death, disease, recovery, relapse or another outcome), are frequently encountered in epidemiologic studies. Censoring is a problem characteristic to most survival data, and requires special data analytic techniques. This course will give an introduction to survival analysis and cover many of the types of survival d…

The number of primary studies evaluating prognostic factors and models is rising. Critically summarizing and analyzing the evidence form prognostic studies in a systematic review and meta analysis, respectively, is beneficial for health care professionals seeking the best evidence. Reviews of prognostic studies are more challenging because of more variation in questions, designs, specific sour…

In the biosciences, response variables are often observed more than once per individual. This enables the researcher to study the development of the variable of interest within individuals, thereby eliminating the variation among individuals, and thus increasing the power of the design. However, since observations on the same individual are almost always correlated, special methods are needed t…

Statistical inference is intended to aid in answering scientific questions about a population, based on a sample from this population, i.e. on data that are subject to variability. The data generating mechanism is described as a probability model that is completely specified except for a limited number of unknown parameters. The questions that can be answered are a) are the data consistent with…

This course offers you the opportunity to practice and improve your skills in analyzing (animal) health data from structured populations. You will perform statistical analyses on a dataset provided by the teachers or on your own data. You will go through the process of formulating a hypothesis, analyzing data, interpretation of the results, and reporting. During the course you will be guided w…