OVERVIEW

To provide advanced skills in applying statistical techniques to biomedical data analysis, with a focus on the design and interpretation of epidemiological and clinical studies.

AIMS AND CONTENT

LEARNING OUTCOMES

To introduce cause-effect reasoning in biomedicine through the study of the main investigation designs. Provide an overview of data analysis techniques in environmental and clinical epidemiology.

AIMS AND LEARNING OUTCOMES

At the end of the course the students will have acquired sufficient theoretical knowledge to identify and apply the most appropriate models for the analysis of biomedical data. In particular:

• adequacy of the model;
• satisfaction of the hypotheses of applicability of the model;
• representation and interpretation of the results;
• implementation of the models in a high-level programming language.

PREREQUISITES

Inferential Statistics, Linear Models (suggested)

TEACHING METHODS

The teaching takes place mainly through frontal lessons. A significant part of the hours is dedicated to PC exercises using the statistical methodologies implemented on the SAS and R software, applied to real cases from the environmental and clinical epidemiological sector.

SYLLABUS/CONTENT

Introduction. Analysis of the causal relationship in biomedicine and epidemiology.

Epidemiological methodology. Observational investigations. Geographical studies and cross-sectional investigation. Analytical cohort and case-control studies. Experimental investigations in clinical epidemiology. Frequency of health outcomes: prevalence, incidence, survival, mortality. Relative frequency indices of health outcomes: rate, risk and odds.

Statistical methods.

• Review of simple and multiple linear regression with continuous and categorical predictors. Introduction to multiplicative models. The log-normal regression model, the ratio of median values and the median percent change.
• The Poisson model for the analysis of health event rates in geographic epidemiology and the analysis of the temporal trend. Average percentage change. Heterogeneity in the counts and the negative binomial model.
• Evaluation of the diagnostic performance of a binary clinical test: signal and noise, conditional probabilities and Bayes formula: sensitivity, specificity, predictive values and prevalence. Evaluation of the diagnostic performance of a clinical test measured on an ordinal or continuous categorical scale: construction and interpretation of a ROC curve.
• The logistic model for binary health events with application to the case-control study. Evaluation of the statistical performance of a logistic model through the ROC curve. Models for survival time analysis: limit product method and Cox regression.

RECOMMENDED READING/BIBLIOGRAPHY

Handouts 

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

September 22, 2025

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

At least three weeks before the examination date, students are provided with a data set which they are asked to analyse according to the instructions in a series of questions. The SAS or R code used for the analysis must be sent to the examination board by email at least two days before the examination. .

The exam is divided into two phases:
Admission phase – The student must answer three multiple-choice questions. Only those who answer at least two questions correctly will be admitted to the next phase.
Oral phase – The admitted students present and discuss the results of the data set analysis in about 20 minutes.

For students with disabilities or with DSA, please refer to the Other Information section.

ASSESSMENT METHODS

The exam is aimed at verifying the student's skills in applying the statistical methods for analysing the data provided, according to the proposed questions.

FURTHER INFORMATION

Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Sergio Di Domizio (sergio.didomizio@unige.it), the Department’s disability liaison.