28172 - BIOSTATISTICS

Conoscenze e abilità da conseguire

Al termine del corso, lo studente conosce le tecniche statistiche applicate. Oltre ad acquisire nozioni elementari di statistica descrittiva, lo studente comprende la logica dell’inferenza statistica, sapendo applicare i test statistici più diffusi nella ricerca e nella professione. Sa inoltre effettuare analisi statistiche con programmi dedicati, e sa interpretare l’output nel contesto del fenomeno o dell’esperimento analizzato.

Contenuti

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
• Contingency tables, odds ratios, relative risk, and Simpson’s paradox
• Descriptive statistics and graphical data exploration
• Probability, diagnostic test evaluation, and key probability distributions
• Statistical inference: confidence intervals and hypothesis testing
• 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.

Testi/Bibliografia

• Wayne W. Daniel, Chad L. Cross, Biostatistics: A Foundation for Analysis in the Health Sciences, 11th Edition, Wiley. Main theoretical reference for the course. Recommended for core statistical concepts, probability and distributions, inference, regression (linear, multiple, and logistic), ANOVA, survival analysis, and nonparametric methods.

• Ronald N. Forthofer, Eun Sul Lee, Mike Hernandez, Biostatistics: A Guide to Design, Analysis, and Discovery, 2nd Edition, Academic Press. Best for understanding study design and interpretation. Recommended for experimental vs observational studies, bias and confounding, data presentation, and introductory statistical reasoning in biomedical contexts.
• Supplementary materials (for PCA, FA, and high-dimensional data) will be provided during the course.

Metodi didattici

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

Modalità di verifica e valutazione dell'apprendimento

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

Strumenti a supporto della didattica

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

Orario di ricevimento

Consulta il sito web di Cinzia Viroli