Data from the Public Health and Medical Research are often subject to clustering either due to the way they are collected, e.g., multiple observations on the same subject over the duration of the observation period (longitudinal data) or due to some other inherent heterogeneity between groups (strata) of the sampling units. Advanced multivariate statistical methods (e.g., Generalized Estimating Equations (GEE) and Mixed-Effects models) have been developed to correctly account for and describe the sources of heterogeneity and variability/correlation structure between and within groups of study subjects. Multivariate statistical methodology involves detecting, analyzing, and characterizing associations among multidimensional data. Related supervised or unsupervised techniques are mainly concerned with the dimension reduction of a system. Center faculty conduct extensive research on novel statistical techniques for analyzing longitudinal and multivariate data including methods for analyzing individual and aggregated results from personalized (N-of-1) trials of treatment interventions, methods for developing and assessing predictive models for ordinal health outcomes.
|Stavroula Chrysanthopoulou||Christopher Schmid|