At the end of the course, students will be better able to avoid the main pitfalls faced by scientists when interpreting the results of data analysis.
The student will also be able to arrange data in a structured way in a spreadsheet and carry out an analysis using the appropriate tools.
Knowledge in the biomedical field is constantly evolving through scientific research in which statistical data analysis is central. The objective of this course is to provide the student with the statistical concepts necessary to critically read the biomedical literature.
1. Analysis bias or how to influence results by choosing analyses a posteriori
2. Publication bias or how to influence knowledge by publishing only what is favorable
3. Combining a priori knowledge and data (Bayesian approach)
4. Correcting the coincidence measure according to the number of attempts (Bonferroni and DFT)
5. Demonstrate equality (equivalence and non-inferiority tests)
6. Build a simple model to explain a continuous response (linear regression)
7. Build a simple model to explain a binary or censored response (logistic and proportional hazards regression)
8. Build a model explaining a response through the effect of several factors (multiple regression)
9. Determine the amount of information to collect, based on response variability and desired precision (sample size estimation)
10. Balance the groups being compared by mixing individuals (randomization).
11.Try to balance the groups compared when analyzing the data (adjustment, weighting, stratification, restriction).
Practical work consists of 8 sessions of 2 hours each.
Attendance is required.
The content of these sessions is as follows:
1. introduction to the spreadsheet
2. Introduction to the spreadsheet
3. Binary data analysis
4. Analysis of continuous data
5. Analysis of censored data
6. Analysis of correlation between continuous data
7. Evaluation
8. Evaluation
Assessment of the theoretical part is based on an in-session written examination. The exam may consist of multiple-answer and/or open-ended questions. The student may be asked to define terms seen in the course, perform calculations that would have been explained in the course (no form is provided, a calculator may be necessary), explain the relevance of concepts seen in the course and/or correctly interpret the results of a data analysis.
Assessment of practical work takes place out of session and does not have a second session.
The final grade (/20) is the sum of the practical grade (/5) and the written exam (/15).
Partial grades do not carry over from one academic year to the next.
| Training | Study programme | Block | Credits | Mandatory |
|---|---|---|---|---|
| Bachelor in Veterinary Medicine | Standard | 0 | 2 | |
| Bachelor in Medicine | Standard | 0 | 2 | |
| Bachelor in Medicine | Standard | 1 | 2 | |
| Bachelor in Veterinary Medicine | Standard | 2 | 2 |