The course is aimed at providing the students with the advanced statistical methods used in the biomedical disciplines and with the main principles of the observational and experimental research in medicine and biology.
Probability theory:
- probability definitions and estimates;
- random variables, probability functions and probability density functions, moments.
Random sampling:
- target population, base of sampling and sample;
- sampling distribution of a statistic.
Crude analysis of the association between a biological response variable and an exposure variable:
- point and interval estimates of the distribution parameters of a biological response variable and of an effect measure: maximum likelihood estimates and confidence intervals;
- statistical hypothesis testing: parametric and non-parametric methods.
Sample size calculations.
Stratified analysis of the association among a biological response variable and exposure variables:
- stratum-specific estimates, testing homogeneity, standardization and pooling, testing the absence of association in any stratum;
- application of stratified analysis methods to survival analysis: non-parametric estimation of the survival function, non-parametric methods for the comparison of the survival function among groups.
Regression models for the analysis of the association among a biological response variable and exposure variables:
- generalised linear models (GLM): linear model for continuous data, logistic model for binary data, exponential model for count and person-time data;
- model checking: assessment of goodness-of-fit and analysis of residuals.
Written exam + oral exam
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