Objectives of Programme's Learning Outcomes

• Describe, organise, analyse and prioritise the phenomena observed in the medical field
• Understand the expression of biological realities in absolute or relative terms, the orders of magnitude, proportions and probability
• Understand and apply mathematical translations of large models and biological phenomena through abstract reasoning
• Understand and use different graphical representations of numerical values and their relationships
• Demonstrate interpersonal skills developed within a medical context
• Understand the basic rules of French (grammar, punctuation, connectors, etc.)
• Develop reasoning skills
• Understand and apply the basic principles of reasoning (obtaining data, analysis, synthesis, comparison, the rule of three, syllogism, analogy, Boolean logic, etc.)
• Understand and apply Bayesian inference
• Use a hypothesis in inductive, deductive or abductive reasoning
• Develop critical thinking, test and monitor conclusions understanding the domain of validity, and explore alternative hypotheses
• Integrate reasoning in clinical approaches
• Manage doubt and uncertainty
• Manage their studies
• Read, interpret, and critique a scientific article
• Be a responsible practitioner
• Base reasoning on the data taken from scientific literature (evidence-based-medicine)

Learning Outcomes of UE

At the end of this course, students will be able to :
- Understand basic principles of statistical hypothesis tests
- Understand basic tools used to assess normality of a distribution
- Compare central tendencies of two or more than two groups
- Compare proportions, use statistical tests of significance and compute odds ratios and related tools
- Analyze the relationship between quantitative variables : tests for linear correlation, linear regression analysis
- Understand basic principles of diagnostic tools used in medicine, and principles of Bayesian reasoning

Content of UE

<u>Basics of statistical hypothesis test</u> : null and alternative hypothesis; unilateral and bilateral tests; power of a test : concept and uses in medicine; number of subjects to include in a trial : basics and practical applications
<u>Tests assessing the normality of a distribution</u> : Shapiro-Wilk and Kolmogorov-Smirnov tests; uses and practical examples; graphical methods including QQ plots; data transformations
<u>Comparisons of two means</u> : uses in medicine; Z-test; Student t test; Fisher's test for comparison of two variances; Welch's test; Mann-Whitney and Wilcoxon tests; tests in case of paired samples : paired t-test and Wilcoxon paired test
<u>Comparisons of two proportions</u> : uses in medicine ; relative risk and odds ratio; confidence interval of an odds ratio; contingency tables; chi2 tests and related tests (Yates correction, Mc Nemar); Fisher's exact test
<u>Comparisons of more than two sample means</u> : uses in medicine; one-way ANOVA : basics, ANOVA table; homoscedasticity, interpretation of the results of an ANOVA test; non parametric approaches (Kruskal-Wallis and Friedman)
<u>Linear correlation in medicine</u> : how to compute r and its confidence interval (Fisher's transformations); tests on r; test on the slope of the linear regression
<u>Diagnostic tests in medicine and introduction to Bayesian reasoning</u>

Prior Experience

Introduction to medical biostatistics (BA1 course)

Type of Assessment for UE in Q1

• Written examination

Q1 UE Assessment Comments

Not applicable

Type of Assessment for UE in Q2

• N/A

Q2 UE Assessment Comments

Not applicable

Type of Assessment for UE in Q3

• Written examination

Q3 UE Assessment Comments

Not applicable

Type of Resit Assessment for UE in Q1 (BAB1)

• Written examination

Q1 UE Resit Assessment Comments (BAB1)

Not applicable