28172 - BIOSTATISTICS

Learning outcomes

At the end of the course, the student knows the applied statistical techniques. In addition to acquiring basic knowledge of descriptive statistics, the student understands the logic of statistical inference and is able to apply the most common statistical tests in research and professional activity. The student is also able to perform statistical analyses with dedicated software and interpret the output in the context of the analyzed phenomenon or experiment.

Course contents

The course introduces fundamental and intermediate statistical methods with applications to biomedical engineering. Topics include:

• Types of biomedical data and measurement scales

• Experimental vs observational study designs, bias and confounding

• Descriptive statistics and graphical data exploration

• Probability, diagnostic test evaluation, and key probability distributions

• Statistical inference: confidence intervals and hypothesis testing

• Contingency tables, odds ratios, relative risk, and Simpson’s paradox

• Linear and multiple regression

• Logistic regression for binary outcomes

• Analysis of variance (ANOVA)

• Survival analysis and nonparametric methods

• Introduction to multivariate techniques: Principal Component Analysis (PCA), Factor Analysis (FA), and high-dimensional data contexts

 

Real biomedical datasets and case studies will be used throughout. The course emphasizes applied data analysis, interpretation, and statistical reasoning, with all analyses conducted using the R statistical software.

Readings/Bibliography

• Wayne W. Daniel, Chad L. Cross, Biostatistics: A Foundation for Analysis in the Health Sciences, 11th Edition, Wiley.
• Ronald N. Forthofer, Eun Sul Lee, Mike Hernandez, Biostatistics: A Guide to Design, Analysis, and Discovery, 2nd Edition, Academic Press.
• Supplementary materials (for PCA, FA, and high-dimensional data) provided during the course.

Teaching methods

The course will combine:

• Lectures to introduce statistical theory and biomedical context

• Case-based discussions to explore applied problems and experimental design

• Hands-on coding sessions using R for statistical analysis

Assessment methods

Assessment will consist in a final written exam with different exercises assessing theoretical understanding, practical analysis and interpretation skills.

Teaching tools

• Lecture slides and reading materials (provided via course platform)
• Statistical software: R and RStudio

Office hours

See the website of Cinzia Viroli