Longitudinal and Incomplete Data Summer School 2025

This is the ninth in a series of annual Summer schools which target people interested in planning and analyzing, e.g. clinical studies. Throughout the course, it is assumed that the participants are familiar with basic statistical modeling, including linear models (regression and analysis of variance), as well as generalized linear models (logistic and Poisson  regression). Moreover, prerequisite knowledge should also include general estimation and testing theory (maximum likelihood, likelihood ratio, etc.). As the annotated programs, output, and illustrations are based on SAS and R, a working knowledge of these software packages is required.