Quantitative Methods to Support Healthcare Decisions
A.Y. 2023/2024
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
Course syllabus
The program is:
1) Point estimates and confidence intervals
2) Statistical inference
3) Comparison between groups
4) The association between categorical variables
5) The simple linear regression model
6) The multiple linear regression model
7) Analysis of variance
8) The logistic regression model
9) Health care data
1) Point estimates and confidence intervals
2) Statistical inference
3) Comparison between groups
4) The association between categorical variables
5) The simple linear regression model
6) The multiple linear regression model
7) Analysis of variance
8) The logistic regression model
9) Health care data
Prerequisites for admission
Basic knowledge of statistics (descriptive statistics, sampling, random variables)
Teaching methods
Theoretical lectures and labs using IT tools.
Teaching Resources
The main books are
· Agresti A., Finlay B. - Metodi statistici di base e avanzati per le scienze sociali, Pearson, ISBN 9788891914484
· Newbold, P . Statistics for business and economics. Pearson. ISBN 978-1-292-31503-4
To integrate:
· Piccolo, D.. Statistica per le decisioni: la conoscenza umana sostenuta dall'evidenza empirica. Società editrice il Mulino, Spa. ISBN 978-88-15-27220-1
The teaching material will be available on the Ariel platform (http://ariel.unimi.it/)
· Agresti A., Finlay B. - Metodi statistici di base e avanzati per le scienze sociali, Pearson, ISBN 9788891914484
· Newbold, P . Statistics for business and economics. Pearson. ISBN 978-1-292-31503-4
To integrate:
· Piccolo, D.. Statistica per le decisioni: la conoscenza umana sostenuta dall'evidenza empirica. Società editrice il Mulino, Spa. ISBN 978-88-15-27220-1
The teaching material will be available on the Ariel platform (http://ariel.unimi.it/)
Assessment methods and Criteria
The final exam consists of an applied part in which the student will answer empirical questions starting from datasets and using the statistical software and a written part in which the student will answer short numerical exercises and theoretical questions.
SECS-S/01 - STATISTICS
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES
Lessons: 40 hours
Professor:
Spinelli Daniele
Educational website(s)
Professor(s)