The course aims at providing the students with the theoretical and practical tools needed to evaluate the frequency of diseases in human populations and the associated risk factors, i.e. expertise in epidemiology, biostatistics and information technology applied to the analysis of biomedical data. Knowledge and understanding: Knowledge and skills concerning the main designs of epidemiological studies, the statistical methods for the analysis of biomedical data and the programming syntax of a statistical software (STATA). Applied knowledge and understanding: a) To know the statistical techniques used for the analysis of biomedical data; b) to perform data analysis using a statistical software (STATA); c) to interpret the results obtained. Making judgements: Ability to choose the appropriate statistical methods in relation to the type of data and the design of the study. Communication skills: Ability to communicate the results of an analysis of biomedical data clearly and concisely. Lifelong learning skills: Ability to apply autonomously the statistical and epidemiological methodologies learned during the course on various biomedical problems.
The course is structured in theoretical lessons (32h) and in practical lessons (24h) on the use of a statistical software (STATA) for the quantitative analysis of biomedical data, which are provided in dual mode (face to face and remote).
The teaching material is made available to the students on the e-learning web page of the course (Moodle platform).
1. Introduction to epidemiology
- Definition and key features
- Traditional classification of epidemiology
- John Snow and cholera outbreaks in London
2. Measures of occurrence
- Cumulative incidence
- Incidence rate
3. Measures of association and public health impact
- Epidemiological associations
- Attributable risk (AR) and AR%
- Relative risk (RR) and Odds ratio (OR)
- Effect modification
4. Types of epidemiological studies
- Ecological studies
- Cross-sectional studies
- Cohort studies
- Case-control studies
- Experimental studies
5. Causal interpretation of an empirical association
- Statistical vs. causal associations
- Causal models in epidemiology
- Validity of a study (random error, bias, confounding)
- Types of bias
- Methods to control confounding
- Hill’s positive criteria for causality
6. Health prevention, screening and diagnostic tests
- Primary, secondary, tertiary prevention
- Validity and performance of a diagnostic test
7. Principles of inference
- Principles of sampling
- Point estimate and sampling distribution
- Confidence interval
- Hypothesis test
- Test of significance
8. Stratified analysis
- Effect modification vs. confounding
- Stratum-specific estimates
- Testing homogeneity
- Pooled estimate
- Testing the stratified null hypothesis of no association
9. Basic statistical models in epidemiological research
- Linear regression model
- Logistic regression model
10. Statistical methods for survival analysis
- Kaplan-Meier non-parametric estimator
- Cox regression model
|Marubini E, Valsecchi MG||Analysing Survival Data from Clinical Trials and Observational Studies||John Wiley & sons||1995|
|Pearce N||A short Introduction to Epidemiology (Edizione 2)||2005||https://vula.uct.ac.za/access/content/group/9c29ba04-b1ee-49b9-8c85-9a468b556ce2/DOH/Module%202%20(Bio_Epi)/Epidemiology/EPIDEMIOLOGY/Pearce.pdf|
|Hennekens CH, Buring JE||Epidemiology in Medicine||Lippincott Williams & Wilkins||1987|
|McCullagh P, Nelder JA||Generalized Linear Models (Edizione 2)||Chapman and Hall/CRC||1989|
|Rothman KJ, Greenland S, Lash TL||Modern Epidemiology (Edizione 3)||Lippincott Williams & Wilkins||2008|
|Glantz SA||Statistica per Discipline Biomediche (Edizione 6)||McGraw-Hill||2007||9788838639258|
The final test is a written exam in presence (computer lab). The test is the same for attending and non-attending students.
The remote exam is however guaranteed for all students who request it in the academic year 2020/21.
The aim of the test is to verify the knowledge of all the topics discussed and the ability to solve a biomedical problem by analyzing health data using the statistical software STATA.
The commands, results and interpretation of the analysis are reported in written form. In addition, students have to answer some questions to ascertain the understanding of theory.
The final evaluation is expressed in thirtieths.