Course syllabus

Analys av överlevnadsdata
Survival Analysis

FMSN10, 7,5 credits, A (Second Cycle)

Valid for: 2013/14
Decided by: Education Board B
Date of Decision: 2013-04-10

General Information

Elective for: F4, F4-bm, Pi4, Pi4-bm, Pi4-mrk
Language of instruction: The course will be given in English on demand


Survival data is common in medical, technical, and economical applications. Data usually consists of the time to some event(s) together with other factors that may influence this time. Often the data are censored (i.e. one can only observe whether the time lies in some interval) and/or truncated (i.e. one can only observe those times that lie in some interval). Therefore, modeling and analysis of such data require special methods. These methods are indispencible in, e.g., the pharmaceutical industry and in clinical and pre-clinical research.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

Competences and skills
For a passing grade the student must

be able to use a statistical programme package for basic studies of survival data in medical statistics, and interpret the result of such studies.

Judgement and approach
For a passing grade the student must


Survival data; censored and truncated data. Covariates.

Distributions and models for survival data. Counting processes and martingale theory.

Estimation of the survival function and cumulative hazard function (Kaplan-Meier and Nelson-Aalen estimators). Non-parametric one- and multiple sample tests. Kernel estimates of the hazard function.

Semi-parametric regression models for data with covariates. The Cox model. Aalen's model. Likelihood-theory for estimation of the Cox model. Projection methods in counting processes for Aalen's model.

Competing risk methods for analysis with several different endpoints.

Bootstrap methods for survival data.

Statistiscal functionals for limiting distributions in survival analysis.

Examination details

Grading scale: TH
Assessment: Oral exam and written project reports. The course grade is av weighting of the results of the exam and the project reports.


Admission requirements:

Required prior knowledge: Inference, e.g. Monte Carlo and Empirical Methods for Stochastic Inference helps.
The number of participants is limited to: No
The course overlaps following course/s: MAS213, MASM21

Reading list

Contact and other information

Course coordinator: Dragi Anevski,
Course homepage:
Further information: The course is also given at the faculty of science with the code MASM21.