Biostatistics
A.A. 2023/2024
Obiettivi formativi
The course aims to introduce students to biostatistics, i.e. the application of statistical principles to questions and problems in genomics, biology or medicine.
Risultati apprendimento attesi
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.
Periodo: Secondo semestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Secondo semestre
Programma
· 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).
Prerequisiti
No prerequisites different from those required for admission to the Master Degree program.
Metodi didattici
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.
Materiale di riferimento
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.
Modalità di verifica dell’apprendimento e criteri di valutazione
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 - PROBABILITA E STATISTICA MATEMATICA
SECS-S/01 - STATISTICA
SECS-S/01 - STATISTICA
Esercitazioni: 24 ore
Lectures: 36 ore
Lectures: 36 ore
Docente:
Cappozzo Andrea
Docente/i
Ricevimento:
Venerdì dalle 10 alle 12
Ufficio n.29, III piano di via Conservatorio