4.00 credits
20.0 h + 16.0 h
Q2
Teacher(s)
Devleesschauwer Brecht (compensates Speybroeck Niko);
Language
English
Content
Module 1: The use of routine data for the generation of epidemiological information
Module2: Review of the basic concepts in epidemiology
Module 3: Bias Control (Bias: revision; Control of confounding (random sampling, pairing, standardization,…); Adjustment by a regression model: example: logistic regression
Module 4: Analyzing and understanding incidence rates (Logistic and Poisson regression)
Module 5: Simulation Modeling in epidemiology
Module 6: Study of some advanced epidemiological approaches and illustrations (Space-time models, Classification and regression Trees; Decomposing the inequalities of health.)
Module2: Review of the basic concepts in epidemiology
Module 3: Bias Control (Bias: revision; Control of confounding (random sampling, pairing, standardization,…); Adjustment by a regression model: example: logistic regression
Module 4: Analyzing and understanding incidence rates (Logistic and Poisson regression)
Module 5: Simulation Modeling in epidemiology
Module 6: Study of some advanced epidemiological approaches and illustrations (Space-time models, Classification and regression Trees; Decomposing the inequalities of health.)
Teaching methods
In English
The lectures will be illustrated by concrete cases extracted from literature. Sessions of exercises will go along with the lectures. The exercises will be conducted in small groups, worked out by the students and discussed together in class. The exercises are simple applications (related to the knowledge acquired in the theoretical part), or exercises combining several principles (related to the teaching objectives) which will allow the use of a diversity of skills and which will be the object of group works at specific times (the methodology will be explained during the course).
Software : R
R is an interactive programming language containing a very large collection of statistical methods and important graphic facilities. It is a free clone of the S-Plus software marketed by MathSoft and developed by Statistical Sciences around the language S. The internet site of the "R core-development TEAM", http://www.r-project.org, is the best source of information on the software R.
The lectures will be illustrated by concrete cases extracted from literature. Sessions of exercises will go along with the lectures. The exercises will be conducted in small groups, worked out by the students and discussed together in class. The exercises are simple applications (related to the knowledge acquired in the theoretical part), or exercises combining several principles (related to the teaching objectives) which will allow the use of a diversity of skills and which will be the object of group works at specific times (the methodology will be explained during the course).
Software : R
R is an interactive programming language containing a very large collection of statistical methods and important graphic facilities. It is a free clone of the S-Plus software marketed by MathSoft and developed by Statistical Sciences around the language S. The internet site of the "R core-development TEAM", http://www.r-project.org, is the best source of information on the software R.
Evaluation methods
Closed book (theory) & open book exam (practical exercise) and excercises during the teaching sessions
Note : 60% exam + 40% data analysis project
Note : 60% exam + 40% data analysis project
Online resources
Documents available on Moodle
"R core-development TEAM", http://www.r-project.org, is the best source of information on the software R.
"R core-development TEAM", http://www.r-project.org, is the best source of information on the software R.
Bibliography
"Statistique/épidémiologie" Ancelle; collection " Sciences fondamentales "; éditions Maloine, Paris (2002). "The Oxford Handboractice" Pencheon, Guest, Melzer, Gray; Oxford University Press; Oxford (2006)
Faculty or entity
FSP
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Interdisciplinary Advanced Master in Science and Management of the Environment and Sustainable Development
