STATISTICS, DATA RETRIEVAL, DATA MINING AND EPIDEMIOLOGY

Federico Ruffinatti (18 h – 3 ECTS)
He received his master’s degree in Physics in 2008 and a PhD in Neurosciences 2014 at University of Turin. He currently works as postdoc researcher at the Department of Pharmaceutical Sciences (DSF, University of Eastern Piedmont, Novara), studying calcium and electrical signals in brain cells. On the theoretical ground his main scientific activities range from bioinformatics and computational neurosciences to the analysis of biological signal coding, while on the experimental side he is involved in the design and validation of new technological platforms for intracellular calcium and membrane voltage imaging.
Academic lecturers: 8

Guest lecturers: 0

Laboratory: 10

Francesco Barone Adesi (12 h – 2 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.
Academic lecturers: 6

Guest lecturers: 6

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.
Academic lecturers: 2

Guest lecturers: 0

Laboratory: 4

Title Statistics, data retrieval, data mining and epidemiology (6 ECTS)
Program

Statistics basics: Random variables, descriptive statistics, distributions, sampling, Student’s t-test, p-values and confidence intervals, error types, statistical power and effect size, ANOVA, post-hoc tests and multiple comparisons, linear regression and GLM framework, non-parametric hypothesis tests, survival analysis. Informatics tools for statistical analysis applied to realistic datasets.

Federico Ruffinatti – 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. and guest lecturers – 12 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

Alan Grafen, Rosie Hails. Modern Statistics for the Life Sciences. OUP Oxford

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:

–        a firm grounding in the foundations of biomedical 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 biomedical 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:

  • A written exam 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. Part 1 will be carried out on the exam day.
  • A practical examination during which each student will be asked to perform statistical analyses on a dataset provided by the instructor of the module entitled “Statistics basics” (25% of the final mark). Part 2 will be carried out on the exam day.
  • Awritten 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 on the day of the exam.

TITLE

Statistics, data retrieval, data mining and epidemiology (6 ECTS)

 

PROGRAM

Statistics basics: Random variables, descriptive statistics, distributions, sampling, Student’s t-test, p-values and confidence intervals, error types, statistical power and effect size, ANOVA, post-hoc tests and multiple comparisons, linear regression and GLM framework, non-parametric hypothesis tests, survival analysis. Informatics tools for statistical analysis applied to realistic datasets.

Federico Ruffinattid – 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. and guest lecturers– 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

Alan Grafen, Rosie Hails. Modern Statistics for the Life Sciences. OUP Oxford

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:

  • a firm grounding in the foundations of biomedical 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 biomedical 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:

  • A written exam 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. Part 1 will be carried out on the exam day.
  • A practical examination during which each student will be asked to perform statistical analyses on a dataset provided by the instructor of the module entitled “Statistics basics” (25% of the final mark). Part 2 will be carried out on the exam day.
  • Awritten 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 on the day of the exam.

Last modified: July 13, 2020