INGLESE

English

- The R programming environment
- Basic Epidemiological Concepts
- Correlation vs Regression
- Comparison of groups
- Factorial design
- Observational studies
- Linear regression and multiple linear regression model
- Logistic regression model
- Some notes on relevant biostatistics topics

- Material provided during the lessons
-Claus Thorn Ekstrom and Helle Sorensen, 2015.
Introduction to Statistical Data Analysis for the Life Sciences (2nd edition). CRC Press. ISBN 9781482238938.
-Cook, T. D., & DeMets, D. L. (Eds.). (2007). Introduction to Statistical Methods for Clinical Trials. doi:10.1201/9781420009965

Educational goals:
Following the general objectives of the Bachelor's Degree in Data Analytics, the EXPERIMENTAL RESEARCH DESIGN course pursues the aim of providing students with advanced knowledge and skills to train professionals with mathematical-statistical skills for understanding and processing life science data. The specific objective of the course is to allow students to become familiar with the statistical-informatics methods for data processing, analysis, forecasting, presentation, and interpretation of the results of some advanced statistical methodologies commonly used in biostatistics.

Expected learning outcomes:
The course aims to provide students with theoretical knowledge and techniques necessary for an in-depth understanding of epidemiology and biostatistics phenomena through the analysis of available data.

Concerning the professional profile that the course of study aims to train, teaching is aimed at developing the following skills:

1) Knowledge and understanding:
- in-depth and specialised knowledge necessary for the collection and organisation of biological data;
- in-depth and specialised knowledge necessary for the analysis of biological data through the statistical software R;
- in-depth and specialised knowledge necessary for the presentation of the results obtained from the analysis of biological phenomena.

2) The autonomy of judgment:
- ability to independently choose the most suitable type of analysis based on the reference context and the type of biological data available;
- ability to interpret the results obtained autonomously through advanced statistical analysis.

3) Communication/application skills:
- to use the appropriate statistical terminology for the type of analysis conducted;
- to apply the acquired knowledge for the diagnosis and understanding of biological phenomena;
- cleverly communicate the results of a statistical analysis based on the objectives to be pursued and the recipient of the report.

It is recommended to know the essential concepts of basic statistics.

Teaching is structured in frontal lessons, divided into theoretical lessons and practical sessions using the R software.

The assessment of students' learning level will be carried out with a computer test and a subsequent oral discussion.
The computer test consists of exercises related to the methods that will be illustrated during the course and can contain some questions about the theory.
The duration of this test will depend on the degree of difficulty of the proposed questions and will be communicated during the course.
The main objective of the practical test is to prove "knowledge" and "know-how". Instead, the oral exam is aimed at probing communication skills, mastering the specific technical language of the discipline dealt with, clarity of exposition and the ability to interpret.
The exam methods are the same for attending and non-attending students. Non-attending students can contact the professor to get hold of the slides of the course, in particular on some topics that are present in the additional suggested books but are not present or are treated marginally in the main book.

- Introduction
- The R programming environment
- Data organisation and the exploratory analysis
- Review of the main probability distributions
- Basic Epidemiological Concepts: study designs, the difference between observational studies and randomized clinical trials
- Absolute Risk, Relative Risk, Odds Ratio, Risk Difference
- Understanding Contingency tables and Chi-square Tests
- Correlation vs Regression, statistical analysis with two, three, or more variables: the main association measures, confounders, mediation, moderation, spurious correlations, the use of graphs in statistics.
- Comparison of groups:
1) Parametric statistical tests: t-test, Welch’s t-test, paired t-test, ANOVA between groups, One-way ANOVA, Two-way ANOVA, ANOVA within groups
2) Non-parametric Tests: Wilcoxon signed-rank test, Wilcoxon matched pair test, Kruskal–Wallis test, Friedman test
- ANCOVA
- Factorial design
- Observational studies
- Linear regression and multiple linear regression model
- Logistic regression model
- The instrumental variable regression model
- Propensity score matching
- Some notes on relevant biostatistics topics: causal inference, potential outcome framework, Bayesian networks, sample size estimation, survival analysis, sensitivity analysis.