18th European Headache Congress

Headache measurements in population-based studies&nbsp;

Professor

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.

Headache measurements in population-based studies

Appointment

Session