Biostatistics
A.Y. 2023/2024
Learning objectives
The course aims to introduce students to biostatistics, i.e. the application of statistical principles to questions and problems in genomics, biology or medicine.
Expected learning outcomes
At the end of this class , the students are expected to:
- know basic techniques and tools for the synthetic and graphical analysis of the information provided by clinical data sets
- apply the methods and techniques of biostatistics to real data sets by means of the use of appropriate statistical software.
- know the basic models for the representation and the analysis of random phenomena, with particular focus on genomics problems, and their application
- be able to apply methods and tools of biostatistics and survival analysis
- apply the methods and techniques of biostatistics to real data sets by means of the use of appropriate statistical software.
- know basic techniques and tools for the synthetic and graphical analysis of the information provided by clinical data sets
- apply the methods and techniques of biostatistics to real data sets by means of the use of appropriate statistical software.
- know the basic models for the representation and the analysis of random phenomena, with particular focus on genomics problems, and their application
- be able to apply methods and tools of biostatistics and survival analysis
- apply the methods and techniques of biostatistics to real data sets by means of the use of appropriate statistical software.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
· The design of experiments: comparing treatments, random allocation, intention to treat, response bias, clustered clinical trials, observational studies, cross-sectional studies, cohort studies, case-control studies.
· Categorical data: the chi-squared test for association, tests for 2 by 2 tables (chi-squared test, Fisher's exact test), Yates' continuity correction for the 2 by 2 table, odds and odds ratios, non-parametric methods, the Mann Whitney U Test, the Wilcoxon matched pairs test, Spearman's and Kendall's rank correlation coefficients, enrichment data, statistical modeling and identification of differential enrichment in genome-wide experiments.
· Time to event data: the Kaplan Meier product-limit estimate of survival, the log-rank test, survival function, hazard function, cumulative hazard function, parametric lifetime models (Exponential, Weibull, Gamma; Log-normal, Inverse Gaussian, Pareto, Gompertz), treatment of missing values, lost to follow up and intention to treat analysis, censoring, Cox Proportional Hazards model, frailty models.
· Meta-analysis: heterogeneity, measuring heterogeneity, mixed effects models (continuous outcome variables, dichotomous outcome variables).
· Categorical data: the chi-squared test for association, tests for 2 by 2 tables (chi-squared test, Fisher's exact test), Yates' continuity correction for the 2 by 2 table, odds and odds ratios, non-parametric methods, the Mann Whitney U Test, the Wilcoxon matched pairs test, Spearman's and Kendall's rank correlation coefficients, enrichment data, statistical modeling and identification of differential enrichment in genome-wide experiments.
· Time to event data: the Kaplan Meier product-limit estimate of survival, the log-rank test, survival function, hazard function, cumulative hazard function, parametric lifetime models (Exponential, Weibull, Gamma; Log-normal, Inverse Gaussian, Pareto, Gompertz), treatment of missing values, lost to follow up and intention to treat analysis, censoring, Cox Proportional Hazards model, frailty models.
· Meta-analysis: heterogeneity, measuring heterogeneity, mixed effects models (continuous outcome variables, dichotomous outcome variables).
Prerequisites for admission
No prerequisites different from those required for admission to the Master Degree program.
Teaching methods
Class lectures and practices; during course practices, also given in an informatics room using the student's laptop, the R program language will be illustrated and used.
Teaching Resources
All the bibliographical suggestions as well as additional material will be available on the "Be e-Poli" (BeeP), the portal for the network activities of students and professors at the Politecnico di Milano, accessible from the Politecnico di Milano Web site; students registered to the course for the current academic year can access it.
Assessment methods and Criteria
The assessment is based on a written exam at the end of the course, with exercises on all the topics presented during the course lectures or practices, and on a data analysis team project evaluation.
MAT/06 - PROBABILITY AND STATISTICS
SECS-S/01 - STATISTICS
SECS-S/01 - STATISTICS
Practicals: 24 hours
Lectures: 36 hours
Lectures: 36 hours
Professor:
Cappozzo Andrea
Professor(s)