The course's objective is to train professionals capable of carrying out research, managing healthcare services, and holding management roles within healthcare organizations.

With this in mind, the expected outcomes are the following:

 Knowledge and Understanding

Students must demonstrate advanced knowledge of statistics applied to biomedical and management research.

Data Analysis: Understand advanced inferential statistical methodologies such as regression and multivariate analysis used in healthcare.

Study Design: Understand cross-sectional, case-control, and cohort epidemiological study designs.

 Ability to Apply Knowledge and Understanding

 Students must be able to apply statistical techniques to solve clinical, managerial, and organizational problems.

 Real-World Data Analysis: Ability to analyze comprehensive datasets from healthcare databases, electronic health records, or clinical trials, including using statistical software.

 Clinical and Healthcare Management: Apply statistics to improve the quality, safety, and effectiveness of treatment/care pathways.

 Research: Translate a research question into a coherent statistical analysis plan.

 Making judgments

 Students must be able to critically interpret statistical results and make evidence-based decisions.

 Critical evaluation: Critically evaluate the methodological and statistical validity of scientific publications.

 Interpretation: Ability to interpret analytical results (p-values, confidence intervals), distinguishing between statistical and clinical significance.

 Ethics: Recognize the ethical implications of healthcare data management and the integrity of results.

 Communication skills

 Students must communicate the results of complex analyses clearly and comprehensibly.

 Reporting: Ability to present epidemiological data and research results to both specialists (physicians, researchers) and non-specialists (management, the public).

 Visualization: Appropriately use graphs, tables, and bar charts to summarize evidence.

 Learning skills

 Students must develop the independent learning skills necessary for ongoing professional development.

 Continuous updating: Ability to identify and learn the use of new statistical methodologies and software in a rapidly evolving field (data science, artificial intelligence applied to health).

 Independent research: Ability to independently conduct literature reviews and bibliographic searches.

interactive frontal lessons with exploration of practical examples evaluation of results

Mandatory

Technical correlations and methodologies; Sampling methodologies and techniques for the extrapolation of a Significant sample, Number of a sample and errors that are made in analyzing a sample; Point estimates, confidence interval; Sime for large samples; small samples and Frequency, Analysis of the data obtained on the basis of acceptable errors; Hypothesis tests for large samples and frequency; Chi Square; Anova; some methods for non-parametric statistics. multivariate analysis. Concrete examples of data analysis in the health sector, drafting of statistical reports. Critically interpret statistical results and make evidence-based decisions. Interpret analysis results (p-values, confidence intervals), distinguishing between statistical and clinical significance. Critically evaluate the methodological and statistical validity of scientific publications. Use specific software for statistical analysis.

Notes prepared by the teacher

Recommended book: 

Exploratory and Inferential Methods; M. Fraire, A. Rizzi; Carocci Editore

Applied Statistics. M. Castino, E. Rolletto. Piccin Editore

Typical exercises on the topics of the program, with evaluation of the results obtained

After reading a given scientific publication, critically evaluate the methodological and statistical validity of the publication

We want to determine whether therapy A is more effective than therapy B given the following data: