Analysis of survival and duration data

lstat2220  2026-2027  Louvain-la-Neuve

Analysis of survival and duration data
4.00 credits
15.0 h + 5.0 h
Q1
> Schedule
Teacher(s)
Van Keilegom Ingrid;
Language
French
> English-friendly
Prerequisites
Concepts and tools equivalent to those taught in the teaching units:
  • LDATS2030 Programmation et data reporting en R
  • LSTAT2120 Linear models
Students should have a good understanding of probability theory and statistics.  They should also have a good command of the R software.
Learning outcomes

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.
 
Content
  • Introduction to basic concepts (such as censoring and truncation mechanisms, certain common parametric survival functions in survival analysis, etc.)
  • Non-parametric estimation of basic quantities (the Kaplan-Meier estimator of the survival function, the Nelson-Aalen estimator of the cumulative hazard function, etc.), the derivation of certain (asymptotic) properties of these estimators, and hypothesis tests concerning the equality of two or more survival curves
  • Proportional-hazards model (estimation of model components, hypothesis testing, selection of explanatory variables, model validation, etc.)
  • Accelerated failure time (AFT) model (estimation of model parameters, hypothesis testing, model selection, model validation, etc.)
Teaching methods
The course comprises lectures and practical sessions. 
Evaluation methods
The assessment consists of a written exam and a computer-based project (analysis of real-world 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.
  • Klein, J.P. et Moeschberger, M.L. (1997). Survival analysis, techniques for censored and truncated data, Springer, New York.
  • Kleinbaum, D.G. and Klein, M. (2005). Survival analysis, a self-learning text, Springer, New York.
Faculty or entity
> LSBA


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Data Science : Statistic
DATS2M
4
Master [120] in Biomedical Engineering
GBIO2M
4
Master [120] in Statistics: Biostatistics
BSTA2M
4
Master [120] in Statistics: General
STAT2M
4
Master [120] in Mathematical Engineering
MAP2M
4
Certificat d'université : Statistique et science des données (15/30 crédits)
STAT2FC
4