Welcome to the analysis of twin data!
The Institute of Public Health, Department of Epidemiology and Biostatistics presents the course Analysis of Twin Data in Health Research with the aim: This course gives an introduction to the modern analysis in studies of twins.
The students will be able to understand founding theory and apply basic methods and biometric models in a research context. The students learn how to investigate genetic and environmental influences on traits under various twin designs and assumptions. They also learn how twins can be used effectively in studying association controlling for various sources of confounding.
The course introduces statistical concepts that are typically targeted in analysis of twin and family data and touches some more advanced methods. Findings and insights from studies with twins will be highlighted. Time is devoted for presentations by researchers including discussion and feedback.
- Classic biometric modelling.
- Time to event analysis of twin data
- Biometric modelling of multivariate outcomes in twins.
- The matched case co-twin design.
- Workshop on contributed projects
- The future of twin studies
- Workshop on contributed projects (Optional day 3 with contributed presentations and discussion)
2 full days. The course is online and physical.
Type of evaluation
Presentation of project report project or multiple-choice web-exam.
Lectures and exercises including use of statistical software R. The typical arrangement is lectures followed by practical sessions.
- Course leader, Jacob Hjelmborg, Department of Epidemiology, Biostatistics and Biodemography, University of Southern Denmark.
- Professor Jaakko Kaprio (Epidemiology, University of Helsinki)
- Professor Jennifer Harris (Epidemiology, University of Oslo)
- Professor Thomas Scheike (University of Copenhagen)
- Professor Qihua Tan and Professor Jacob Hjelmborg (University of Southern Denmark)
Knowledge about statistics corresponding to what is acquired from a graduate level health science course or alike. Experience in using statistical software is an advantage.
The course is free of charge for PhD students enrolled in Universities that have joined the "Open market agreement".
For other participants there is a course fee of DKK 5249,-