Quantitative Methods to Support Healthcare Decisions
A.Y. 2025/2026
Learning objectives
The learning objectives of the course aim to develop the knowledge, the comprehension, and the use of statistical methods for the data analysis. The students will learn how statistics could answer to research questions in healthcare decision.
In the course, the students will study the analysis of single events and the evaluation of possible relations across events or variables, with a focus on the inferential problems.
It will be emphasized the importance of formulate interpretative hypothesis as a starting point of each quantitative data analysis. The different theoretical topics will be studied also from an operational point of view by developing practical examples and real data through statistical software.
In the course, the students will study the analysis of single events and the evaluation of possible relations across events or variables, with a focus on the inferential problems.
It will be emphasized the importance of formulate interpretative hypothesis as a starting point of each quantitative data analysis. The different theoretical topics will be studied also from an operational point of view by developing practical examples and real data through statistical software.
Expected learning outcomes
At the end of the course, the student will be able to know the main statistical methods and the advanced techniques for data analysis. The student will be able to develop an analysis on real data, from the choice of the data and of the most appropriate statistical methods to the comprehension and interpretation of the results.
Lesson period: Third trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Course syllabus
1. Descriptive statistics
- Statistical variables
- Tables and graphs
- Measures of central tendency
- Measures of variability
- Summary measures for grouped data (covariance and correlation)
2. Probability and discrete and continuous random variables
3. Point and interval estimation
4. Hypothesis testing
5. Comparison between two groups
6. Simple and multiple linear regression
7. Comparison among groups: Analysis of Variance (ANOVA)
8. Goodness-of-fit tests
9. Analysis of association between categorical variables
10. Introduction to simple logistic regression
- Statistical variables
- Tables and graphs
- Measures of central tendency
- Measures of variability
- Summary measures for grouped data (covariance and correlation)
2. Probability and discrete and continuous random variables
3. Point and interval estimation
4. Hypothesis testing
5. Comparison between two groups
6. Simple and multiple linear regression
7. Comparison among groups: Analysis of Variance (ANOVA)
8. Goodness-of-fit tests
9. Analysis of association between categorical variables
10. Introduction to simple logistic regression
Prerequisites for admission
The course does not require prior knowledge of statistics, but a basic understanding of mathematics is recommended.
Teaching methods
The teaching methods of the course include:
· lectures
· guest talks by experts of the sector
· presentations of assignments
· practical exercises
The course consists of 40 hours divided into twoparts: one dedicated to descriptive statistics and basic techniques of statistical inference (point and interval estimation and hypothesis testing) - 20 hours, Dr. Facchinetti - and the other dedicated to more advanced inference topics, such as Analysis of Variance (ANOVA), goodness-of-fit tests, and the analysis of associations between categorical variables - 20 hours, Dr. Brusa.
Each session takes 4 hours (including "academic quarter of hours"). Lectures and practical exercises will take place during the sessions.
Classes will be held at the times indicated on the student timetable portal at the following link:
https://easystaff.divsi.unimi.it/PortaleStudenti/index.php
Classes will be held in person, and attendance is strongly recommended.
In addition to lectures, the course includes practical sessions aimed at applying the statistical concepts covered.
· lectures
· guest talks by experts of the sector
· presentations of assignments
· practical exercises
The course consists of 40 hours divided into twoparts: one dedicated to descriptive statistics and basic techniques of statistical inference (point and interval estimation and hypothesis testing) - 20 hours, Dr. Facchinetti - and the other dedicated to more advanced inference topics, such as Analysis of Variance (ANOVA), goodness-of-fit tests, and the analysis of associations between categorical variables - 20 hours, Dr. Brusa.
Each session takes 4 hours (including "academic quarter of hours"). Lectures and practical exercises will take place during the sessions.
Classes will be held at the times indicated on the student timetable portal at the following link:
https://easystaff.divsi.unimi.it/PortaleStudenti/index.php
Classes will be held in person, and attendance is strongly recommended.
In addition to lectures, the course includes practical sessions aimed at applying the statistical concepts covered.
Teaching Resources
- P. NEWBOLD-W.L. CARLSON-B.M. THORNE, Statistica, Pearson.
- Other materials on ARIEL website.
- Other materials on ARIEL website.
Assessment methods and Criteria
Preparation and discussion of a report on a project carried out in collaboration with external experts (maximum of 3 credits), plus a written exam consisting of theoretical questions and exercises covering both the first and second parts of the course.
The report must be developed during the course period under the supervision of the experts through online meetings via Teams and presented on the last day of class in front of the instructors and the entire class.
The report must be developed during the course period under the supervision of the experts through online meetings via Teams and presented on the last day of class in front of the instructors and the entire class.
SECS-S/01 - STATISTICS - University credits: 3
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES - University credits: 3
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES - University credits: 3
Lessons: 40 hours
Professors:
Brusa Luca, Facchinetti Silvia
Educational website(s)
Professor(s)