To understand the goal of a study, the importance of proper study design, of actions to be taken to avoid bias, and of appropriate sample size.
To talk with a statistician and anticipate the information he needs as part of the design and analysis of a study.
To recognise the type of design (e.g. parallel group, crossover, factorial) and the type of output variable (quantitative, qualitative, independent data, paired data). Detect the presence of potential covariables.
To deduce the kind of statistical analysis to be performed and do the analysis using R (statistical package) if the level of complexity of the experimental design and/or statistical model allows it.
The training consists in 4 modules:
M1 – Introduction. Basic principles and vocabulary used in applied statistics. Role of statisticians in clinical and preclinical trials. Main steps to the design of experiments and analysis of results. Normal distribution and theory, descriptive statistics (parameters, graphics).
M2 – Comparison of means. T-test and analysis of variance in the case of independent data or paired data. Multiple comparisons of means. Understanding of difference versus equivalence testing approaches.
M3 – Regression analysis. Introduction to the simple linear regression and various validity criteria. Introduction to polynomial models.
M4 – Comparison of proportions. Introduction to 2 x 2 contingency table and related statistical tests. Comparison of mean proportions and presentation of various statistical metrics used in clinical and preclinical trials (e.g. sensitivity, specificity).
There are many exercises. Students answer to the questions with the support of the teacher and perform the statistical calculations using R (statistical package). Students also do some homework and can find the solutions on WebCampus.
There is a 3-hour written exam, which usually consists in 5 to 6 exercises. The students don’t have to use R but should be able to understand tables of results and graphical outputs found in articles or statistical reports. The students have access to their notes.
The exact modalities of the evaluation are likely to be modified during the preparation of the examination schedules, depending on the practical constraints with which the faculty administration may be confronted, or in the event of illness / force majeure / encroachment with an internship, or because of the health situation related to the coronavirus.
Training package (slides) and R codes associated with exercises are available on WebCampus.
R Development Core Team (2005). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL: http://www.R-project.org
Text Books:
Dagnelie P. [2013]. Statistique théorique et appliquée. Tome 1. Statistique descriptive et bases de l'inférence statistique. Bruxelles, De Boeck, 517 p. ISBN 978-2-8041-7560-3
Dagnelie P. [2011]. Statistique théorique et appliquée. Tome 2. Inférence statistique à une et à deux dimensions. Bruxelles, De Boeck, 736 p. ISBN 978-2-8041-6336-5
| Training | Study programme | Block | Credits | Mandatory |
|---|---|---|---|---|
| Standard | 0 | 4 | ||
| Standard | 0 | 4 | ||
| Standard | 0 | 4 | ||
| Standard | 1 | 4 | ||
| Standard | 1 | 4 | ||
| Standard | 1 | 4 |