509310 - ADVANCED BIOSTATISTICS

courses

ID:

509310

Duration (hours):

24

CFU:

SSD:

STATISTICA MEDICA

Year:

2025

Date/time interval

Primo Semestre (01/10/2025 - 16/01/2026)

Course Objectives

The course aims to develop in the student the theoretical-practical knowledge of the most frequent statistical-epidemiological methodologies (knowledge and understanding), as well as the ability to correctly apply this knowledge to the analysis of experimental and epidemiological studies (ability to apply knowledge and understanding). At the end of the course the student will be able to use the main statistical data analysis tools, to interpret the results deriving from the application of advanced statistical methodologies in a conscious and critical way, to communicate the evidence deriving from the analyses themselves.

Course Prerequisites

To follow the course the student should have basic knowledge of Epidemiology and Medical Statistics

Teaching Methods

The teaching activity includes lectures with a problem solving approach and exercises with applications in R to data sets, to answer specific research questions and to help the student obtaining the necessary skills for planning studies and biostatistic analyzes

Assessment Methods

The verification of knowledge and skills will be made up of a single written exam for the 3 sub-sections. Verification of learning will take place on a shared platform and generally provides for the solution of at least 3 exercises for each sub-section making up the course both by applying in R and interpreting the output of R. The time available is 180 minutes. The outcome is an eligibility and will be obtained if achieved on all 3 sub-sections. In accordance with Italian regulations, students with specific learning disability are entitled to the compensatory measures recommended by the SAISD Centre of Pavia University.

Texts

- Daniel WW, Cross CL. Biostatistica. EdiSES, 2019.
- Data Mining with R: Learning with Case Studies, Second Edition Luis Torgo - CRC Press
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani , e al. Springer
- Genetic Epidemiology Methods and Protocols Editors: Evangelou, Evangelos (Ed.)

The course is organized in 3 distinct modules.

Module 1 - Principles of Statistical Analysis with R

• Lesson 1 Title: "The programming environment R generalities and main objects"
- R installation and configuration
- Syntax and main objects (Vectors, Lists, Matrices, Data Frames)
- Import data into R
- Treatment of missing data
- Univariate descriptive measures
- Laboratory with R

• Lesson 2 Title: "Graphic representation with R"
- Types of graphs and choice of the appropriate graphical representation
- Basic package
- Grid package
- ggplot2 package
- Laboratory with R

• Lesson 3 Title: "Data Mining with R"
- Techniques for reducing the dimensionality of data
- Exercises with R

• Lesson 4 Title: "Identifying the relationships between variables with R"
- Statistical methods to evaluate the relationships between variables
- Exercises with R

Module 2 - Statistical methods in genetic epidemiology

• Lesson 1 Title: "Association studies in Genetics"
- Concepts of genetic epidemiology "the study design"
- Statistical approaches to evaluate the association
- Exercises with R

• Lesson 2 Title: "Power calculation and sample size"
- Calculation of power and sample size with R
- The problem of multiple tests

• Lesson 3 Title: "Association studies in Genetics"
- Association analysis and transmission models
- Exercises with R

• Lesson 4 Title: "Genome Wide Association Studies"
- Complex diseases and GWAS
- Methodology in GWAS
- Exercises with R

Module 3 - Survival analysis
When using the survival analysis.
Kaplan-Meier survival estimates.
How to determine if there is a different survival: the Log-rank test.
Cox's model of proportional hazards: when it can be applied and what information it provides. How to determine which model is best.
Notes on the stratified Cox model.

Course Language

Italian

More information

Students who will attend the course are asked to have a laptop available for exercises in R.
The teachers receive students only by appointment after sending an email to
simona.villani@unipv.it
davide.gentilini@unipv.it
luisa.bernardinelli@unipv.it