At the end of the course, the student will be able to understand and apply more advanced biostatistical techniques in the setting of complex observational studies. The student will be able to design and develop a statistical analysis plan, to carry out basic analysis using the statistical package SAS, to verify the findings, and to interpret and report the results in a manner that is meaningful to clinicians and applied health scientists. The course will be given from an applied point of view; more theoretical understanding will be touched upon, but only to the extent of useful in applications and for understanding the models. This course will cover 3 advanced topics:

BLOCK I. Complex Survival Models: Aim is to understand and analyse multivariate survival data when subjects transit between multiple states; including time-dependent covariates recurrent episodes of disease time-to-failure of two “linked subjects” (e.g. kidneys, hips, twins).

BLOCK II. Longitudinal Models and Multi Level Models: Aim is to understand and analyse linear and generalized linear models in the presence of longitudinal and repeated measurements hierarchical structured, nested or clustered data.

BLOCK III. Prediction Models: Aim is to design a prediction model in the setting of binomial outcome and survival data. The process includes building a model for prediction purpose applying validation tools developing a user friendly tool /calculator to be used by clinicians in a day-to-day practice.