STATISTICS, DATA RETRIEVAL, DATA MINING AND EPIDEMIOLOGY
Federico Ruffinatti (18 h – 3 ECTS)
Guest lecturers: 0
Laboratory: 10
Francesco Barone Adesi (12 h – 2 ECTS)
Guest lecturers: 6
Laboratory: 0
Sarah Cargnin (6 h – 1 ECTS)
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:
|
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