- Docente: Rossella Miglio
- Credits: 10
- SSD: SECS-S/05
- Language: Italian
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in STATISTICAL SCIENCES (cod. 8055)
Learning outcomes
The aims of this course are to introduce the basic concepts of survival analysis and to explain and illustrate how survival analysis is applied to biomedical data.
Course contents
Introduction to the analysis of survival data in biomedical research, censoring and truncation. Estimation of the integrated survivorship functions with non parametric methods: Kaplan-Meier product-limit and lifetable methods.
Hazard function: definition ed estimate. Relationship between hazard, survivorship and density funcion in survival data analysis.
Comparison of survivorship functions: log-rank and Wilcoxon rank sum test.
Semiparametric Cox regression model: definition, assumptions, estimation and hypothesis tests; interpretation of estimated coefficients and residual analysis.
Extensions of the proportional hazard model: stratification and time-varying coefficients.
Parametric regression models. The exponential regression model. Weibull, Log-normal, log-logistic and generalized gamma regression models.
Accelerated failure time data.
Competing risk models. Frailty model.
Longitudinal data analysis.
Case studies
Readings/Bibliography
D. COLLETT, Modelling survival data in medical research, Chapman & Hall, 2003
D. W. HOSMER, S. LEMESHOW, Applied Survival Analysis: Regression
Modeling of Time to Event Data, Wiley, New York, 1999.
E.T.LEE, Statistical Methods for Survival Data Analysis, Wiley, New York, 2003
Teaching methods
Traditional lessons
Seminar
Computer session
Assessment methods
Computer laboratory test and oral examination
Teaching tools
Computer session
Office hours
See the website of Rossella Miglio