# Biostatistics and Clinical epidemiology

A.Y. 2020/2021
3
Max ECTS
36
Overall hours
SSD
MED/01
Language
Italian
Learning objectives
The aim of the course is to develop knowledge and skills related to the concept of probability, to deal with situations of decision-making uncertainty, to descriptive and inferential statistical methods, necessary to address the problem of biological variability, to aspects relating to critical judgment on the quality of studies published in scientific literature, from which to extract evidence for clinical decisions in the practice of Evidence Based Medicine (EBM).
The student will learn to deal with outcomes of stochastic experiments in terms of probability, having clarified the concepts of the event universe, elementary event and compound event, conditional event, applying the sum and product rules and the Bayes theorem to derive the probability a rear. He will also learn to use the concept of random variable, in the various probability distribution models useful for statistical inference (Gaussian, binomial, poissonian, hypergeometric, Student's t, chi-square model) and to interpret the values of the estimators, calculated on data samples, to make a correct inference, as regards the corresponding parameters of the population.
The student will also learn to recognize the different types of study used in epidemiological-clinical research (observational and experimental studies, cross-sectional and longitudinal studies, cohort and case-control studies), to assess their quality (confounding bias, in observational studies, information bias in experimental studies) and to extract information useful for clinical decision (accuracy measurements from diagnostic studies, estimates of association between exposure and disease from etiological and prognostic studies, measures of efficacy and therapeutic safety from comparative therapy studies).
The student will acquire the skills necessary to set up a database on a spreadsheet and proceed to the calculation of the necessary descriptive and inferential statistics (comparison of averages, simple linear regression analysis, comparison of proportions, analysis of simple and multiple contingency tables, adjustment techniques for confounding, calculation of confidence intervals).
Expected learning outcomes
The student will be able to read a scientific publication at the end, correctly interpreting the various sections of which it is composed: introduction, for the completeness and quality of the premise of the study, for its rationale and objectives; the materials and methods, for the eligibility and exclusion criteria that serve to define the target population, for the definition of the variables detected and considered in various ways in the study, for the appropriateness of the statistical analysis methods; the results, for the critical reading of tables and indexes to be interpreted in their practical meaning and for their correct interpretation for the purposes of application to the clinic; the discussion, due to the different possible interpretations of the results, with particular attention to the weak aspects of the study.
Course syllabus and organization

Responsible
Professor(s)