Biostatistics

Summary

This course covers statistical methods that are widely used in medicine and biology. A key topic is the analysis of longitudinal data: that is, methods to evaluate exposures, effects and outcomes that are functions of time. While motivated by real-life problems, some of the material will be abstract

Content

Keywords

Biostatistics; statistical inference; survival analysis; longitudinal data; research synthesis

Learning Prerequisites

Required courses

The students are expected to have taken introductory courses in statistical theory, probability theory and regression modeling.

Recommended courses

Undergraduate courses in statistics.

Important concepts to start the course

Likelihood theory, statistical testing. Experience with R is an advantage, but is not required.

Learning Outcomes

By the end of the course, the student must be able to:

• Identify statistical methods that are suitable for answering a given scientific problem.
• Justify why a statistical method is applied to given problem.
• Apply methods that have been taught in the course.
• Critique evaluate published studies and methodologies.

Transversal skills

• Communicate effectively with professionals from other disciplines.
• Access and evaluate appropriate sources of information.
• Demonstrate the capacity for critical thinking

Teaching methods

Classroom lectures, where I will use Beamer slides and the blackboard. Exercises and take-home projects that will require programing in R.

Assessment methods

Final written exam and continuous assessment.

Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.

Supervision

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

Teaching resources

• Aalen, O., Borgan, O. and Gjessing, H., 2008. Survival and event history analysis: a process point of view. Springer
• Andersen, P.K., Borgan, O., Gill, R.D. and Keiding, N., 2012. Statistical models based on counting processes. Springer

Ressources en bibliothèque

• Andersen Statistical models
• Aalen survival and event history

Moodle Link

• https://go.epfl.ch/MATH-449