ITA | ENG
B024218 - BIOSTATISTICS
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2018-19
Course year
Second year - Second Semester
Belonging Department
Statistics, IT and its applications "G. Parenti" (DISIA)
Course Type
Single education field course
Scientific Area
MED/01 - MEDICAL STATISTICS
Credits
6
Teaching Hours
48
Teaching Term
25/02/2019 ⇒ 31/05/2019
Attendance required
No
Type of Evaluation
Final Grade
Course Content
show
Course program
show
Lectureship
Teaching Language
Italian. In the presence of foreign students, the opportunity of teaching in English will be evaluated.
Course Content
Statistical methods for the analysis of bio-medical and epidemiological data.
Suggested readings (Search our library's catalogue)
Slides, codes, scientific papers will be provided during the course.
Learning Objectives
1) To provide a general overview of the main study designs adopted in biostatistics and epidemiology
2) To encourage the critical discussion of the results
3) To introduce the student to novel/recent statistical approches in the fields of epidemiology and biostatistics
2) To encourage the critical discussion of the results
3) To introduce the student to novel/recent statistical approches in the fields of epidemiology and biostatistics
Prerequisites
Students attending the course should have basic knowledge of the main methods for the analysis of epidemiological and bio-medical data and of the Bayesian approach. They should be able to write R codes.
Teaching Methods
Lectures, practical sessions (R software), homeworks
Type of Assessment
- Revision and evaluation of the assigned homeworks
- Final discussion with the teacher about the topics of the course
- Final discussion with the teacher about the topics of the course
Course program
- Observational and experimental studies
- Confounding and effect modification
- Statistical analysis of cohort and case-control studies: stratification and regression
- Survival analysis in the presence of competing events (Aalen-Johansen estimator, Fine and Gray model)
- Spline and Generalized Additive Models (GAM)
- Penalized regression methods (ridge regression, Lasso, Elastic Net)
- Meta-analysis
- Confounding and effect modification
- Statistical analysis of cohort and case-control studies: stratification and regression
- Survival analysis in the presence of competing events (Aalen-Johansen estimator, Fine and Gray model)
- Spline and Generalized Additive Models (GAM)
- Penalized regression methods (ridge regression, Lasso, Elastic Net)
- Meta-analysis