Course type: compulsory
Lecturer: Maja Pohar Perme, Ph.D., Professor
 

For the timeline see Curriculum.

Prerequisites:

• Enrolment into the first year of the programme.
• Prerequisites to the written exam are the successfully completed homeworks.

Content (Syllabus outline):

• Censoring.
• Survival curve, hazard function.
• Exponent and Weibull distribution.
• Nonparametric estimation of survival curve.
• Comparison of survival curves.
• Parametric regression models in survival analysis.
• Model of proportional hazards.
• Model definition and assumptions, parameter interpretation.
• Parameter estimation, partial likelihood method.
• Stratification.
• Time dependent variables.
• Estimation of survival probability.
• Test statistics.
• Residuals.
• Goodness of fit.
• Model predictive value.
• Competing risks.
• Multistate models.

Objectives and competences:
Event history analysis (survival analysis) is a field of statistics that deals with times between the events. The times are often censored which means that the event does not happen (no death, no job loss, machine failure... ). That and the observed time varying factors require specific methods for the analysis. 
The student gets acquainted to the methods of the survival analysis that cover most of the needs in practice (in biostatistics as well as humanities and technical fields).

Intended learning outcomes:
By the end of the course students should be able to recognize the problems that censoring brings and understand why the more basic statistical techniques are insufficient with such data. They should be able to define the goals of their study, choose the appropriate method, use it and understand what information it can and cannot give.