18th European Headache Congress

Objective:
This one-hour course aims to provide participants with a concise overview of key health data science tasks—description, prediction, and causal inference—within the context of headache research. Additionally, it will introduce the concept of confounding and its implications in epidemiological headache research.

Overview:
The session will introduce the three health data science tasks: description, prediction, and causal inference. Participants will begin by exploring descriptive methods, which involve summarizing and characterizing data to understand the burden and distribution of headache disorders within a population and discuss potential threats. Following this, the course will cover the fundamentals of prediction models, focusing on techniques used in predicting headache outcomes or treatment success. The third segment will address causal inference, explaining methods to determine cause-and-effect relationships in headache research. In the final part of the course, the concept of confounding will be introduced, highlighting how confounding can distort the observed associations between exposure and outcome. This session will equip attendees with a basic understanding of identifying and accounting for confounding in their research.

Breakout room 1

Neuroepidemiology: the basics

Teaching course

TC chair

Objective:
This one-hour course is designed to familiarize participants with methods to control confounding and introduce the target trial framework within the context of headache research.

Overview:
The session will start with a detailed examination of various tools to control for confounding, emphasizing multivariable analysis techniques. Participants will also learn the basics of propensity score methods, including propensity score matching, stratification, and weighting. The latter part of the course will introduce the target trial framework, a conceptual approach that aims to design observational studies in a way that emulates a hypothetical randomized controlled trial. This framework provides a structured methodology for causal inference, offering a robust way to draw conclusions about cause-and-effect relationships in real-world headache research settings. By the end of this course, participants will understand how to apply these advanced methods to enhance the validity of their epidemiological studies on headache disorders.

Tobias Kurth

Chair

Invited speaker

TC speaker