4.00 credits

15.0 h + 5.0 h

Q1

Teacher(s)

Van Keilegom Ingrid;

Language

French

> English-friendly

> English-friendly

Prerequisites

Concepts and tools equivalent to those taught in teaching units

LSTAT2020 | Logiciels et programmation statistique de base |

LSTAT2120 | Linear models |

Learning outcomes

| |

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 (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 course consists of lectures and exercise sessions. Recorded videos in English are available on Moodle.

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

Learning outcomes

Master [120] in Biomedical Engineering

Master [120] in Statistics: Biostatistics

Master [120] in Mathematics

Master [120] in Statistics: General

Master [120] in Mathematical Engineering

Certificat d'université : Statistique et science des données (15/30 crédits)