Biomedical sciences

Module Medical Statistics

Knowledge and understanding
At the end of the course, students will acquire advanced knowledge of the principles of biostatistics applied to research in the health sciences, with particular reference to nursing and midwifery care contexts. Students will gain a solid understanding of the main models and methods of medical statistics, techniques for the analysis of health data, and the fundamentals of statistical inference and hypothesis testing, also in relation to the design and evaluation of clinical and epidemiological studies.

Applying knowledge and understanding
Students will be able to apply appropriate statistical methods to the description, analysis, and interpretation of healthcare and clinical data, correctly using statistical indicators, tables, and graphical representations. They will also be able to understand and critically evaluate scientific studies published in the biomedical literature, recognizing the role of statistical methodologies in the production and interpretation of scientific evidence supporting evidence-based healthcare practice.

Making judgements
Students will develop the ability to critically interpret the results of statistical analyses reported in the scientific literature, evaluating their methodological appropriateness, potential sources of bias, and the limits of statistical inference. These skills will enable them to contribute, within the health professions, to the evaluation of the quality of scientific evidence and to support decision-making processes in organizational, clinical, and research contexts.

Communication skills
Students will be able to appropriately use statistical and methodological terminology and communicate the results of quantitative analyses clearly and rigorously, both orally and in writing, including in the presentation and discussion of research projects and scientific work.

Learning skills
By the end of the course, students will have acquired the methodological competencies necessary to independently deepen their knowledge of statistical techniques applied to healthcare research and to critically interpret the scientific literature, laying the foundations for lifelong learning and participation in research and healthcare service evaluation activities.

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.

The assessment consists of a structured written test with 13 multiple-choice questions covering the course content, administered via the Exam.net digital platform.

Each of the first 12 questions has one correct answer and is worth 2.5 points, for a maximum of 30 points. The 13th question is used exclusively for the possible award of honors (cum laude) and is considered only if the student achieves full marks in the first 12 questions.

The test is designed to assess students’ knowledge and understanding of the course topics, as well as their ability to apply acquired knowledge to the analysis and interpretation of discipline-specific issues. It also evaluates the ability to identify relationships between key concepts and to use appropriate scientific terminology.

An oral examination may be required at the instructor’s discretion.

Grading Criteria

The final grade is expressed on a 30-point scale and is based on the number of correct answers in the first 12 questions (2.5 points each).

A score of 30/30 is awarded for 12 correct answers. Lower grades are assigned proportionally based on the number of correct responses.

A minimum of 8 correct answers (18/30) is required to pass the exam. Higher scores reflect a progressively stronger level of knowledge (e.g., 10 correct answers = 25/30; 11 correct answers = 28/30, after rounding).

The final grade directly reflects the level of mastery of the course content.

Honors (cum laude) may be awarded to students who achieve 30/30 and correctly answer the 13th question.