9 August 2014
Event History and Survival Analysis
In event history analysis (and survival analysis, which is the name used mostly in bio sciences, where the methods were first applied) we are interested in time intervals between successive state transitions or events. Typical examples are: duration of unemployment, duration of marriage, recidivism in criminology, duration of political systems, time from diagnosis to death, and so on. The most distinctive feature of time to event data is that the event is often not observed at the time of analysis. Applying standard statistical methods to such data leads to severe bias or loss of information. Special methods are therefore needed to extract information which we are used to get using standard methods (formally this means estimating the distribution function and incorporate predictive variables into such estimation). Further complications arise when covariates change in time, when times between recurring events are correlated, when there are competing risks, or when effects change in time.
Participants should have some working knowledge of linear regression models and be familiar with the basics of inferential statistics. If not, a crash course in inferential statistics is highly recommended. As for mathematics, it is understood that the participants will not have much mathematical skills, but for the exceptions the written material will contain more rigorous treatment of the subject. Still, I would suggest that the participants clear the dust from the mathematics lying buried in their memory, preferably with the notion of the integral included.
To obtain the full five ECTS credits available, participants must complete their course, which includes a project assignment and an exam, on 9 August 2014. Participants who do not sit the exam can still obtain three ECTS credits by completing the project assignment. Participants who sit the exam but choose not to complete the project assignment can obtain two ECTS credits.
EUR 925: ECPR member
EUR 1250: ECPR non-member