Three post-conference workshops will take place on March 21, 9:00 - 13:00. Registration is required as only 25 places per workshop are offered on a first-come, first-served basis. The fee for a workshop is 10€.

Please bring your own laptop. A limited number of laptops can be provided by HMGU. If you want to make use of one of HMGU laptops please contact the organizers () in advance. 

Please note that there is no catering for the workshops except for some cookies, thus please bring your own food and drinks. There is also the possibility to buy something at the mensa which is directly next to the conference building. Also, the tap water has a very good quality if you want to refill your own bottles.



Application of DAGs in epidemiological research

Lecturer: Frauke Hennig

As environmental epidemiologist struggle to find a sufficient set of variables they need to adjust for in their models in order to report an unbiased exposure effect estimate, directed acyclic graphs (DAGs) provide a helpful tool to identify a sufficient adjustment or potential sources of biases. This workshop will provide an application-based framework of why, and more importantly how to use DAGs for their research question and how to evaluate them regarding both prior knowledge and observational data.

Statistical strategies for multi-pollutant modeling

 Lecturer: Jelle Vlaanderen

Analyzing mixtures of exposures is a recognized challenge in environmental epidemiology. In this workshop we will introduce and provide R script for several statistical approaches for multi-pollutant modeling and modeling of exposomics data, including sparse partial least squares regression and (Bayesian) penalized regression. These modelling approaches mitigate co-exposure confounding bias and generally yield improved selection accuracy (fewer false positive discoveries) than conventional single-pollutant regression methods.

Survival analysis with R

Lecturer: Sofia Rodopoulou, Matteo Scortichini

The main scope of the workshop is to familiarize participants with the methods used in the analysis of time to event data using R language. Emphasis will be given to the peculiarities that characterize the time to event data analysis, as censoring, to the use of semi-parametric models and the correct interpretation of the results. Methods to deal with heterogeneity between groups of observations (e.g. frailty models) will be also introduced as well as methods to deal with coefficients and/or covariates changing over time.