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.
3 credits
20.0 h + 10.0 h
Q2
Teacher(s)
Elens Laure;
Language
French
Prerequisites
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Main themes
The objective of this course is to give a basic knowledge in the statistical data processing related with the biomedical domain. The course also deals with how computer software, in particular JMP (SAS) can be used to present and analyze data.
Aims
At the end of this learning unit, the student is able to : | |
| 1 | This course is designed to introduce the students to the statistical and methodological issues applied to problems in the biomedical sciences and to avoid the common pitfalls in data analysis. |
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
IIntroduction to statistical methodology.
Summarizing and presenting data in tables and graphs
- Extract and organize electronically stored data
- Produce useful graphical and numerical summaries
Univariate statistics
- Descriptive aspect (median, standard deviation, variance, interval of confidence)
- Validation aspect (test on normality of distribution, discordance tests on outliers, precision, accuracy)
- Significance tests: type 1 and type 2 errors
- Capability analysis
Bivariate analysis: one-way and two-way ANOVA
- Descriptive aspect: multiple box-plot, means or medians
- Validation aspects: normal distribution of residuals, detection of outliers.
- Significance tests: type 1 (t test, Tukey test or Dunnett test) and type 2 (power test).
Linear regression model
- Parameter determination.
- Validation aspect: limit of detection and quantification.
- Inverse prediction
Non-linear regression
- Kinetic models
- Michaelis-Menten and Hill models
- Pharmacokinetic models
- Dissolution models
MANOVA and repeated measures analysis of variance
Multivariate statistical methods:
Logistic regression and ROC curves
Survival analysis
Exercises with statistical software (JMP)
- Use of an intranet site to illustrate the course (slides, JavaScript illustrations, summary) and the exercises (exercises, solutions to exercises, tables of statistics).
- Connections with clinical and biomedical applications.
Other information
Prerequisites: mathematical and basic statistical notions.
Evaluation based on the treatment or the discussion of examples issued from the scientific literature in the medical or pharmaceutical field.
Staff: 1 professor /20 students for the practical exercises.
Teaching aided with computer, practical exercises with statistical software JMP
Faculty or entity
FARM
