Cursussen

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…

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’, which takes place 23 – 27 September at the LUMC. For more information and the option to register, please visit the website of Boerhaave Nascholing.

This course covers the fundamental toolbox of biostatistical methods plus a solid methodological basis to properly interpret statistical results. The course is a mandatory part of the LUMC Graduate School Program and a prerequisite for any of the advanced biostatistical courses offered by the LUMC. This is a basic course, targeted at a wide audience. The course is given during a single week fro…

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…

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…