Methods and Data AnaLysis (MEDAL) in Biomedical Research
Monza
Master 2 Level
English
12 months
blended
Vision
The Master in MEthods and DAta anaLysis in Biomedical research (MEDAL) is a unique study program that combines expertise in innovative study design with quantitative methods for data analysis in the field of Biomedical Research.
The training program of the Master aims at giving solid bases on the principles of study design and of appropriate methodologies for analysing data in preclinical, translational and clinical research in the context of precision medicine and clinical epidemiology.
Mission
The course will introduce concepts related to statistical methods and bioinformatics in a multidisciplinary approach that connects statistical models and exploratory methods typical of artificial intelligence.
As the field of health care continues its move toward precision medicine, new study designs, infrastructure and resources are needed to advance the effective use of biomedical data and information to improve human health. The master has a multidisciplinary approach combining statistical modelling, machine learning techniques, artificial intelligence, and bioinformatics tools.
The program offers an international classroom – a mix of students with diverse backgrounds, some beginning their career, others already having gained experience in the biomedical research.
Teaching Organization
280 hours of Lezioni
400 hours of Stage
Exams and Attendance
Courses
- Welcome and introduction
- Taxonomy of the studies
- Descriptive statistics
- Introduction to R
- Probability, sensitivity, specificity and ROC curve
- Statistical Distribution Functions; Inference: hypothesis testing & confidence interval
- Variable transformation
- Analysis of Variance (ANOVA)
- Principles of regression and models for continuous data
- Effect measures with binary endpoints
- Regression models for binary response
- Variable selection
- Prediction models
- Bayesian Analysis
- Missing data
- R software
- Multiple testing
- Survival Analysis
- Design of RCTs; Early phase study design; Biomarker based designs; Sample Size Calculation; Adaptive designs; Bayesian designs; Estimands; Study PASS (Post Authorization Safety Study) and PAES (Post Authorization Efficacy Study).
- Fundamental ethics principles in research; Data governance and regulatory landscape (GDPR); issues and models related to data access / data sharing; The Ethics and Law of Experiments on Animals; End of life issues and the rights of terminally ill patients; The role of Ethical Committees.
- Regulatory aspects & legislations: the view of AIFA; Regulatory aspects & legislations: the view of pharma; Regulatory aspects & legislations in the area of rare-diseases; Good clinical practices.
- Epidemiological designs;
- Introduction to Real World Data;
- Geo-spatial analysis;
- Real World Data (Pharmaceutical);
- Introduction to causal models.
- Protocol development;
- End Point;
- End point Surrogate;
- Organizing Data & Data Management, Case Report Form & ECRF;
- Safety issues;
- Safety data analysis;
- Analysis of longitudinal data;
- Practical Review (Def. of a protocol);
- Reporting results;
- Reproducibility;
- Meta-analysis
- Introduction to machine learning;
- Unsupervised learning;
- Neural networks and deep learning;
- From data to models in pandemics;
- Interpretable and explainable artificial intelligence;
- Computational intelligence for biomedicine and healthcare;
- Bayesian and causal networks.
- Basic principles of scientific writing
- How to write a scientific paper
- How to write a Review
- Case report
- Letter to the editor
- Authorship
- Cover letter
- Peer review and manuscript evaluation
- Communicating science
- Writing an effective abstract
- Poster and oral presentation
- Communicating science to general public
- Social Media Communication
- Funding opportunities for Young Investigators in Italy and Europe;
- Understanding the review process: writing for success;
- How to develop an Irresistible idea;
- The writing schedule;
- Background and Review of Literature;
- Hypothesis, Aim of the study, Preliminary Results;
- Experimental design & methods;
- Time & deliverables;
- Pitfalls;
- Expected outcome;
- Dissemination;
- Future directions
What skills do you gain?
Methods and Data AnaLysis (MEDAL) in Biomedical Research
Who is it aimed at ?
The training provided in MEDAL is of relevance for clinical researchers in universities and hospitals who are involved in "non-profit" and "profit" trials that aim at optimising diagnosis and treatment approaches and also for personnel from CROs (Contract Research Organisations) and public and private research centres and companies involved in biomedical and translational studies.
With the fast development of new technologies, biomedical research has turned into a digitised and data-intensive science. To meet this, we need to develop and train the next generation of medical statisticians and researchers with quantitative skills who can design and implement research in the field of health science.
The need for advanced skills in data science continues to be high in both the public and private sectors, particularly in contexts that emphasise research in public health, clinical medicine, life sciences, biomedicine and biotechnology.
Training objectives:
- To gain experience in the analysis and modelling of data from multiple sources in their application to biomedical research studies.
- To develop precision medicine research studies, including study design and study protocol development, target population, sample size definition, data analysis plan, data collection methods, data management and quality control approaches.
- Learn to implement classical statistical techniques, Bayesian and modern computational methods, and use scientific thinking in the interpretation of results.
- Know how to manage both classical studies (clinical trials) and clinical epidemiology and real-world studies also by integrating clinical big data or molecular biology and genetics in translational research.
- Collaborate and communicate effectively with researchers in related disciplines by developing the ability to work in multidisciplinary teams.
40
STUDENTS
TRAINED
+560
TRAINING
HOURS
PROVIDED
2
EDITION
NUMBER
11
COMPANIES
INVOLVED
%
LEVEL OF SATISFACTION WITH THE COURSE ACCORDING TO AlmaLaurea
%
EMPLOYMENT RATE FOR EACH MASTER ACCORDING TO AlmaLaurea
Professional opportunities
Professionals
The MEDAL Master in Biomedical Research contributes to the technical and scientific training of professionals with a high capacity to design and analyse clinical databases with statistical tools, machine learning and data mining.
Biomedical researcher
The Master contributes to the advanced training of medical statisticians and researchers with quantitative skills who work or want to work in biomedical research.