HEALTH DATA SCIENCE

Aim of this program is that of offering some scenario elements for one of the main application of data science: health systems. In parallel, it will be the opportunity to examine the more common statistical methodologies and tools in this field, both from a theoretical point of view and from a practical one through lab activities.

Knowledge of information systems for health systems. Analysis skills on statistical methods for health data.

Statistical Learning for Data Science.

1. Health systems information needs and (Statistical) Health Information Systems
2. Statistical models for health data (ref. Agresti and Collett books)
- Analyzing contingency tables and comparing proportions. Relative risk, odds ratio, and chi-squared test of independence
- Logistic regression. Interpretation, evaluation, and selection. Categorical predictors and aggregated data.
- Loglinear models for contingency tables. Interpretation, evaluation, and selection. Measures of association and independence. Relationship with logistic regression.
- Introduction to survival analysis. Survival function, Hazard function, censoring.
- Accelerated Failure Time models, proportional hazard models, Cox regression (brief reference)
3. Health data analysis lab
- Case studies and practical applications with R.

Agresti, A. (2018). An introduction to categorical data analysis. John Wiley & Sons. (ch 2, 4, 7)
Collett, D. (2015). Modelling survival data in medical research. CRC press. (ch 2 and 3)

For attending students: students will discuss a workgroup partially prepared during the lab part of the course.
Non-attending students: students will discuss practical work on data given at the exam (written and oral).

Frontal lectures (theory and practice)

English

written and oral

This subject deals with topics related to the macro-area "Poverty and inequalities" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development