Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
4 credits
15.0 h + 5.0 h
Q1
Teacher(s)
Van Keilegom Ingrid;
Language
French
Prerequisites
The student should have good knowledge of probability and statistics. In addition, a good understanding of SAS or R (or any other advanced software) is required.
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
The aim is to familiarize the student with the basic concepts and models in survival analysis. Moreover, by making use of computer packages, the student will be able to solve real data problems. The course stresses more the methodology, the interpretation, and the mechanisms behind common models in survival analysis, and less the theoretical and mathematical aspects. |
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
- Introduction to basic concepts (like censoring and truncation, common parametric survival functions,…)
- Nonparametric estimation of basic quantities (Kaplan-Meier estimator of the survival distribution, Nelson-Aalen estimator of the cumulative hazard function,...), the development of some (asymptotic) properties of these estimators, and hypothesis testing regarding the equality of two or more survival curves
- Proportional hazards model (estimation of model components, hypothesis testing, selection of explanatory variables, model validation, ...)
- Accelerated failure time model (estimation of parameters in model, hypothesis testing, model selection, model validation,...)
Teaching methods
The theory sessions will be given in the form of video sessions in English that are available in Moodle. Question and answer sessions will be organized via Teams, and exercise sessions will take place live in a computer room.
Evaluation methods
The evaluation consists of an oral exam (in order to test the general understanding of the course) and of a project on computer (analysis of real data).
Other information
Slides of the course can be downloaded from Moodle.
Bibliography
- Cox, D.R. et Oakes, D. (1984). Analysis of survival data, Chapman and Hall, New York.
- Hougaard, P. (2000). Analysis of multivariate survival data. Springer, New-York.
- Klein, J.P. et Moeschberger, M.L. (1997). Survival analysis, techniques for censored and truncated data, Springer, New York.
Faculty or entity
LSBA
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Biomedical Engineering
Master [120] in Mathematics
Master [120] in Mathematical Engineering
Master [120] in Statistic: Biostatistics
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master [120] in Statistic: General