STATISTICS, DATA RETRIEVAL, DATA MINING AND EPIDEMIOLOGY
Maurizio Rinaldi (18 h – 3 ECTS)
Maurizio Rinaldi obtained a PhD degree in Elementary Particles Physics at the International School for Advanced Studies in Trieste (Italy).
Afterwards he got Postdoctoral fellowships at Harvard University, Cambridge (MA), USA and a Postdoctoral fellowship at the Milan University (Italy).
He as been researcher of Mathematical Analysis (1995-1999) and subsequently from 1999 Associate Professor of Geometry and from 2004 in Complementary Mathematics.
He taught in Harvard University, Università degli studi di Trieste, and Università del Piemonte Orientale.
He worked in different areas of mathematical physics, geometry and analysis.
He also devoted his attention to the teaching of mathematics to students of not math-oriented undergraduate courses.
Currently he works as a mathematician and statistician with research groups of his University applying statistical learning techniques, bioinformatics and simulation of complex systems in different topics of research mainly in the chemical-biological area.
Guest lecturers: 0
Laboratory: 10
Francesco Barone Adesi (6 h – 1 ECTS)
After a PhD in Environmental Medicine at University of Bari, Italy, in 2012 he received a post-graduate degree in Medical Statistics at the University of Milan, Italy. He was a Post-doctoral fellow at the National Institutes of Health, Bethesda, USA and a Lecturer in Epidemiology, St. George’s, University of London, UK. Currently, he is associate professor of public health (UPO). His main research interests include pharmacoepidemiology, evaluation of public health interventions, with a special interest to interventions aimed to the promotion of healthy aging trough life-style modifications, occupational and environmental epidemiology, statistical methods for epidemiology and public health, systematic reviews and meta-analyses.
Guest lecturers: 0
Laboratory: 0
Sarah Cargnin (6 h – 1 ECTS)
She received her degree in Pharmacy in 2011 and her PhD in Pharmaceutical and Food Biotechnologies in 2016 at UPO. At present, she is a post-Doc in Pharmacology at the Department of Pharmaceutical Sciences in Novara. Her main research interest focuses on pharmacogenetics, systematic reviews and meta-analysis.
Guest lecturers: 0
Laboratory: 4
| Title | Statistics, data retrieval, data mining and epidemiology (5 ECTS) |
| Program |
Statistics basics: random variables, descriptive statistics, probability, distributions and statistical inference. Working with data: use of the free statistical software R for statistical analysis. Federico Rinaldi M. – 18 hours (10 hours of laboratory) Epidemiology: epidemiological methods in public health; epidemiological study designs (clinical trials, cohort studies, case control, transversal and descriptive studies); sources of uncertainty (role of chance, bias and confounding). Barone Adesi F. – 6 hours Systematic reviews and meta-analysis: systematic search for literature evidence, meta-synthesis of qualitative and quantitative data, risk of bias assessment. Cargnin S. – 6 hours (4 hours of laboratory) |
| Textbooks |
OpenIntro Statistics (https://www.openintro.org/book/stat/). Kenneth J. Rothman. Epidemiology: an introduction. Oxford University Press. The slides of the lectures and additional material such as scientific publications will be available to students. |
| Objectives |
The course aims to provide: – basic concepts in statistics and epidemiology; – the know-how to perform systematic review and meta-analysis. |
| Prerequisites | None. |
| Teaching methods | Lectures and hands-on practicals. |
| Expected Results |
The students will be able to: – determine the appropriate statistical procedures for specified data sets and independently perform statistical analysis of biological data; – understand and interpret epidemiological studies; – critically interpret and perform systematic reviews and meta-analysis. |
| Exam modality |
The exam mark will be composed of three parts:
|
TITLE
Statistics, data retrieval, data mining and epidemiology (5 ECTS)
PROGRAM
Statistics basics: random variables, descriptive statistics, probability, distributions and statistical inference. Working with data: use of the free statistical software R for statistical analysis.
Rinaldi M. – 18 hours (10 hours of laboratory)
Epidemiology: epidemiological methods in public health; epidemiological study designs (clinical trials, cohort studies, case control, transversal and descriptive studies); sources of uncertainty (role of chance, bias and confounding)
Barone Adesi F. – 6 hours
Systematic reviews and meta-analysis: systematic search for literature evidence, meta-synthesis of qualitative and quantitative data, risk of bias assessment.
Cargnin S. – 6 hours (4 hours of laboratory)
TEXTBOOKS
OpenIntro Statistics (https://www.openintro.org/book/stat/).
Kenneth J. Rothman. Epidemiology: an introduction. Oxford University Press.
The slides of the lectures and additional material such as scientific publications will be available to students.
OBJECTIVES
The course aims to provide:
- basic concepts in statistics and epidemiology;
- the know-how to perform systematic review and meta-analysis.
PREREQUISITES
None
TEACHING METHODS
Lectures and hands-on practicals.
EXPECTED RESULTS
The students will be able to:
- determine the appropriate statistical procedures for specified data sets and independently perform statistical analysis of biological data;
- understand and interpret epidemiological studies;
critically interpret and perform systematic reviews and meta-analysis.
EXAM MODALITY
The exam mark will be composed of three parts:
- An online examination consisting of multiple-choice questions (50% of the final mark). Individual instructors (3 in total) will prepare questions from their lectures. The number of questions for each module will be proportional to the amount of time the instructor lectured. This part will be carried out on the exam day and will be delivered through DIR.
- An online examination during which each student will have to perform statistical analyses (25% of the final mark). This part will be carried out on the exam day and will be delivered through Moodle (DIR).
- A written essay to be done at home assigned by the lecturer of the module entitled “Systematic review and meta-analysis” (25% of the final mark). Each student will be asked to write the protocol of a systematic review, followed by a meta-analysis, on a topic of his/her choice. The essay must be delivered within the day of the exam.
Last modified: February 14, 2022