Survival Analysis (ΣΕΕ13)

General

School	School of Science
Academic Unit	Department of Mathematics
Level of Studies	Graduate
Course Code	ΣΣΕ13
Semester	1
Course Title	Survival Analysis
Independent Teaching Activities	Lectures-Laboratory (Weekly Teaching Hours: 3, Credits: 7.5)
Course Type	Specialized general knowledge
Prerequisite Courses	-
Language of Instruction and Examinations	Greek
Is the Course Offered to Erasmus Students	Yes (in English, reading Course)
Course Website (URL)	See eCourse, the Learning Management System maintained by the University of Ioannina.

Learning Outcomes

Learning outcomes	This course describes the various methods and underlying theory used for modeling, analyzing and interpreting survival data. Students taking this course will master their understanding of survival techniques and will be able to: understand different types of censoring, and learn to estimate and interpret survival characteristics. compare survival rates in different groups. assess the relationship of risk factors and survival times using the Cox regression model, and assess the appropriateness and adequacy of the model. understand issues in the design, analysis, and interpretation of studies involving time-dependent covariates.
General Competences	Working independently Decision-making Production of free, creative and inductive thinking Criticism and self-criticism

Syllabus

Types of censored data: right censoring, left censoring, interval censoring, e.t.c. Functions of interest in survival studies: The survival function, the hazard rate function, the cumulative hazard rate function and their connection with the probability density function and the cumulative distribution function. Estimation of the survival function: the Nelson-Aalen estimate, the Kaplan-Meier estimate. Parametric estimators. Comparison of two or more survival curves. Estimation of the hazard rate function: parametric estimates, kernel (nonparametric) estimation. Semiparametric estimation: Cox regression and its extensions. Model properties and limitations, variable selection, model validation measures (residual types and analysis, remedial measures).

Teaching and Learning Methods - Evaluation

Delivery

Classroom (face-to-face)

Use of Information and Communications Technology

Use of ICT in communication with students

Teaching Methods

Activity	Semester Workload
Lectures	39
Working independently	78
Exercises-Homework	70.5
Course total	187.5

Student Performance Evaluation

Final written exam in Greek (in case of Erasmus students in English).

Attached Bibliography

Lawless, J.L. (2002), Statistical Models and Methods for Lifetime Data 2nd Edition, Wiley.
Cox D.R. and D. Oakes (1994), Analysis of Survival Data, Chapman and Hall,
Klein and Moschberger (2003). Survival Analysis: Techniques for Censored and Truncated Data, 2nd edition. Springer.
Kleinbaum, D. G. and Klein M., (2005), Survival Analysis: A Self-Learning Text, 2nd Edition. New York: Springer
Therneau T, and Grambsch, P. (2000). Modeling Survival Data: Extending the Cox Model. New York: Springer.
Hosmer, Jr, DW. and Lemeshow, S. (2008). Applied Survival Analysis: Regression Modeling of Time to Event Data, 2nd Edition. Wiley,
Kalbfleish, JD. and Prentice, RL. (2002). The Statistical Analysis of Failure Time Data. Wiley,
Collet, D. (2003). Modeling Survival Data in Medical Research. London: Chapman and Hall.
[Περιοδικό / Journal] Lifetime data analysis