Biomedical sciences

Module Medical Statistics

The course aims to introduce students to the basic principles of medical research, where the object of study is not a single individual but a collective. Students will acquire the ability to understand literature articles with concrete examples applied to clinical practice. The overall objective is to understand the main medical statistics topics relevant to the degree program. Students will acquire knowledge of the main medical statistics models and theorems and their ability to apply them correctly to the qualitative and quantitative description of real-world cases through hypothesis testing. Students will also acquire the ability to independently expand and deepen their understanding of medical statistics topics and their applications.

Traditional lectures, with the support of slides and educational videos of some theoretical-practical topics.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Scientific Rationale: "Why Study Statistics?" What is Statistics? What questions does Statistics answer? Relationships between variables (direct, reciprocal, spurious, indirect, conditional). The phases of scientific research. Variables, measurement scales, and associated graphs.

Definition and objectives of Epidemiology. Frequency measures (absolute frequency, ratios, proportions, crude and specific rates, cumulative incidence, incidence rates, person-time incidence, rates standardized by direct and indirect standardization). Risk measures and their interpretation (cumulative incidence, relative risk, attributable risk, odds ratio).

Study of cause-and-effect relationships. Definition and management of confounding factors and effect modifiers. Main epidemiological studies: cohort studies, case-control studies, cross-sectional studies, and ecological studies. Experimental studies. Reviews and meta-analyses. Introduction to evaluative epidemiology.

Introduction to data analysis. Data preparation and cleaning. Definition of descriptive and inferential statistics. Data representation. Measures of central tendency and variability. Mean, median, mode, range, standard deviation, interquartile range. Skewness and kurtosis. Extreme values ​​and outliers of the mean and median.

Estimation theory and statistical inference. Limits and confidence intervals. Hypothesis testing. "p" values ​​and statistical significance. Position of the American Statistical Society regarding the use of p-values.

Simple frequency tables for unrelated and orderable characteristics, simple frequency tables for quantitative characteristics grouped into classes, double frequency tables. Bivariate analysis, measures of association and measures of correlation. Scatterplot and correlation coefficient. Partial correlation. Notes on nonlinear correlation. Comparison between groups, parametric and nonparametric tests. Basic assumptions for t-tests and ANOVA.

Chi-square test, Mann-Whitney test: comparison of two independent samples. Wilcoxon test: comparison of two dependent samples. Kruskal-Wallis test: comparison of three or more independent samples. Multivariate analysis: linear regression, logistic regression. Survival analysis. ROC curve.

Only the slides prepared by the teacher and provided to students via Studium will be used.