1. SAS programming for management of clinical data
2. Statistical analysis of categorical and continuous biomedical data using SAS       
3. Survival analysis using SAS
4. Bayesian regression analysis for biomedical data with SAS
5. Simulating data with SAS
6. Reporting of clinical data with SAS ODS
7. Use of the SAS macro language process automation
8. Implementation of statistical algorithms in SAS/IML (interactive matrix language)

The MSc in Biostatistica – Biostatistics offers advanced training in design, management, analysis, statistical interpretation and evaluation of experimental studies, observational studies, and surveillance systems in the fields of human and animal health, including biology, biotechnology, population studies, veterinary medicine, preventive, clinical and rehabilitative medicine, environmental sciences.

The duration of the course is 2 years.

Here a detailed description of the Master program.

The Master program’s educational path assures students the SAS Academic Specialization in Advanced Data Analysis in Biostatistics Digital Badge if they pass the following courses:

Linear Models for Categorical Data

Generalized Linear Model

Bayesian Inference

Statistical Models Applied to Clinical Trials

SAS Programming for Biostatistics*

*This course can be not necessary.

N.B. To request detailed information on the issuing of the Badge, please to contact the Coordinator, Prof.ssa Antonella Zambon: antonella.zambon@unimib.it

                                                                                   ATTENTION!

It's very important that the request must be submitted at least within two weeks of obtaining the degree.

E' molto importante che la richiesta sia inviata almeno due settimane prima dal conseguimento della Laurea.

Here a brief summary of the content of each course [check out the links for a more complete description]

Linear Models for Categorical Data

The course introduces the linear models for categorical data according to two different settings. The first concerns the general linear model (GLM), including several special cases such as ANOVA and ANCOVA models. The second setting deals with the generalized linear models, in particular Poisson log-linear models for count data and binomial logistic models, in a GLM perspective. Analyses of empirical cases are carried out through the SAS software.

Generalized Linear Model

The course aims at introducing at the specification, estimation and verification of the interpretative advanced linear models compared to the classical linear model. The course activity comprises theoretical lecture and lab activity with SAS and R.

Bayesian Inference

The course provides knowledge on the basic and advanced statistical principles under the Bayesian paradigm. The methods are illustrated according to an integrated approach with classical statistical inference. R and SAS code to carry out the analyses will be introduced.

Statistical Models Applied to Clinical Trials

The aim of the course is to deepen the student’s knowledge of the statistical design and analysis of a clinical trial. Special focus will be given to the analysis of survival data using SAS, and to the design of simulation studies using SAS.

SAS Programming for Biostatistics

The aim of the course is to deepen the students’ knowledge of the SAS tools useful for data management, statistical analysis, and reporting of clinical and observational studies.

The SAS macro language and SAS/IML (interactive matrix language) will be also introduced.