Courses

Have you ever been frustrated that evidence-based promising innovations have not been adopted and implemented in your healthcare setting? If you would like to tackle this frustration, our course is perfectly suitable for you! In health care, promising research results or evidence-based guidelines do not automatically become standard of clinical care. Therefore, a next step, also known as imple…

This week-long, project-based course aims to provide students with an understanding of advanced methods used in decision-analytic modeling and cost-effectiveness analyses. These include topics like the latest methods for calibration and validation, quantifying uncertainty, and consideration of heterogeneity of patient benefits and equity issues. The course combines lectures and readings to give…

This course covers statistical methods to be used when one or more 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. In a meta-analysis, the study can be regarded as the experimental unit and the observations of patients within the same s…

Competing risks and multi-state models play an increasingly important role in the analysis of time to event data. Regarding competing risks, there is a lot of confusion regarding the proper analysis. The most important reason for the confusion is conceptual: which quantities can be estimated and what do they represent. Once the concepts are understood and the proper type of analysis has been ch…

The digital universe is expanding continuously. This huge amount of information is often referred to big data have a huge potential to answer questions that could not be answered before. For example, in biomedical sciences, researchers increasingly make use of these Big Data, by pooling real-time data from multiple sources including electronic healthcare records in order to e.g. detect diseases…

This course will address two important topics in causal epidemiologic research, namely confounding and effect modification. These topics will be discussed in the context of both etiologic research and observational intervention research. Effect modification and confounding are difficult concepts to understand and often mixed up. In this course the theory as well as the practical side of these i…

Hoewel onderzoekers hun best doen om ontbrekende gegevens te voorkomen, is het een veelvoorkomend probleem in medisch en epidemiologisch onderzoek. Hoe groot de invloed van de ontbrekende gegevens is op de studieresultaten en hoe hiermee om te gaan, is afhankelijk van de hoeveelheid aan ontbrekende gegevens en van de reden waarom de gegevens ontbreken. In deze 3-daagse cursus worden eenvoudige …

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…

Het doel van een predictiemodel is de kans op een bepaalde uitkomst zo goed mogelijk te schatten (voorspellen). Predictiemodellen worden vaak met het oog op de klinische praktijk ontwikkeld, waarbij voor individuele personen de kans op ziekte of herstel door het combineren van informatie over patiënten kan worden berekend. Het model kan dan in de vorm van een predictieregel (clinical prediction…

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…