Event History and Survival Analysis

Short Bio Janez Stare graduated from the Faculty of Mathematics, University of Ljubljana, then held a Master Degree and Ph.D. in Biostatistics from the Faculty of Medicine, University of Ljubljana. He is currently full Professor of Biostatistics at the Faculty of Medicine, Ljubljana, and Head of the Institute of Biostatistics and Medical Informatics, Faculty of Medicine, Ljubljana. His research interests are explained variation in survival analysis, linear models in survival analysis, predictive ability of regression models in survival analysis, frailties, random effects in survival models, relative survival. His personal web page is here http://ibmi.mf.uni-lj.si/janez/index.php?lang=en-GB. Prerequisite knowledge Note from the Academic Convenors to prospective participants: by registering to this course, you certify that you possess the prerequisite knowledge that is requested to be able to follow this course. The instructor will not teach again these prerequisite items. If you doubt whether you possess that knowledge to a sufficient extent, we suggest you contact the instructor before you proceed to your registration. Participants should have some working knowledge of linear regression models and be familiar with the basics of inferential statistics. If not, for the latter a short course in inferential statistics is highly recommended. As for mathematics, it is understood that the participants will not have much mathematical skills, but for those that will, the written material contains more rigorous treatment of the subject. Even though I use the formulas to explain the concepts, 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. Not being afraid of the formulas is an advantage and certainly helps in understanding the subject better. Short course outline 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. In this course we will thoroughly study a situation when there is only one event per subject, but will also review the extensions to a sufficient degree for students to be able to continue their work in the area. Roughly half of the time will be devoted to practical exercises, for which the package R will be used. Familiarity with R is not assumed, but students will receive a short introductory material to the package before the summer school begins. While it is impossible to avoid all formulas, I will focus on the concepts in my lectures, but will support the lectures with more rigorous written material.