Course description
The course will introduce statistical methods to reduce bias and explore causal relationships in modern epidemiology, with an emphasis on exercises conducted in either R or Stata. It will introduce the concept of the counterfactual approach for interpreting causality. Students will be taught techniques based on this approach to investigate mediating factors that contribute to causal relationships. Additionally, students will learn about Inverse-Probability-Weighting, G-computation, and Targeted maximum likelihood estimation as a means to reduce bias caused by confounding or selection. These technique offers several advantages compared to conventional methods that aim to control for systematic bias. The course will address causal considerations in time-to-event analyses and how pseudo-observations can be applied. The students will also gain an understanding of the importance of assessing interactions on both a multiplicative and additive scale and will be introduced to techniques to assess interactions on each scale. Finally, the course will delve into methodologies in quantitative bias analysis, such as introducing E- and G-Values. These tools are valuable for evaluating the robustness of associations against potential unmeasured or uncontrolled confounding.
The course targets PhD students who are familiar with and experienced in basic and conventional epidemiological methods and want to advance their toolboxes by getting introduced to methods in modern epidemiology.
Before the course, participants are expected to read the paper listed in the literature list, which will be provided two weeks in advance.
Intended learning outcomes
Gain advanced knowledge and skills in reducing bias and exploring causal relationships in epidemiological studies, including the application of the counterfactual approach and techniques for investigating mediating factors, interactions on both multiplicative and additive scales, using pseudo observations, and performing quantitative bias analyses.
Teaching methods
The teaching approach will involve a combination of lectures and practical exercises, including hands-on exercises using Stata or R, depending on your preference.
Course fee
The course is free of charge for PhD students enrolled in Universities that have joined the "Open market agreement"
For all other participants the course fee is:
DKK 3000
EURO 402