- Docente: Rossella Miglio
- Crediti formativi: 6
- SSD: SECS-S/01
- Lingua di insegnamento: Inglese
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Bologna
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Corso:
Laurea Magistrale in
Statistical Sciences (cod. 9222)
Valido anche per Laurea Magistrale in Statistical Sciences (cod. 9222)
Conoscenze e abilità da conseguire
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.
Contenuti
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. Time to event analysis of high-dimensional data.
7. Case studies
Testi/Bibliografia
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
Metodi didattici
Lectures and tutorials
Modalità di verifica e valutazione dell'apprendimento
The oral 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.
Strumenti a supporto della didattica
PC/ video projector
Orario di ricevimento
Consulta il sito web di Rossella Miglio
SDGs
L'insegnamento contribuisce al perseguimento degli Obiettivi di Sviluppo Sostenibile dell'Agenda 2030 dell'ONU.