Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Good health and well-being Reduced inequalities Climate Action

Academic Year 2021/2022

Learning outcomes

By the end of the course the student learns the advanced methods of survival analysis and is able to understand how survival analysis is applied to biomedical and social data.

Course contents

1. Revision of parametric and semi-parametric models for time to event data.

2. Competing risk models for continuous and discrete time.

2. Frailty models for continuous and discrete time

3. Multi state models.

4. Models for repeated events.

5. Clinical trial and Propensity score methods in survival analysis.

6. Longitudinal data analysis.

7. Case studies


D. W. HOSMER, S. LEMESHOW, S. MAY, Applied Survival Analysis: Regression Modeling of Time to Event Data, Wiley, New York, 2011.

D. Kleinbaum, Survival analysis, Springer Verlag, 2012.

Handhouts and specific papers provided by the teacher

Teaching methods

Lectures and tutorials

Assessment methods

The exam aims at testing the student's achievement of the following learning outcomes:

- deep knowledge of the statistical methods described and discussed during the lectures

- ability to use these methods in the analysis of survival data

- ability to use the obtained results for the quantitative interpretation of the studied data.

The oral exam focuses on questions concerning topics described and discussed during the lectures.

Teaching tools

PC/ video projector

Office hours

See the website of Rossella Miglio