Advanced Epidemiologic Methods: Causal Research and prediction modeling

Event for expert public only

Intensive Short Course

Learning objectives of this intensive short course

By the end of this week, participants should be able to:

• Critically assess the results of epidemiological studies on causal relationships or prediction models
• Correctly define exposures and learn how to best represent them in models
• Understand difference between various sources of bias (confounding, measurement error and missing data) and the way these biases may differentially affect studies on causal relationships and prediction models.
• Describe key assumptions of methods used to control for (time-varying) confounding.
• Describe key assumptions of methods used to handle missing observations.
• Understand the reasons for and consequences of overfitting prediction models
• Describe recent developments in the fields of causal research and prediction modelling

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