Linear Mixed and Generalized Linear Mixed Models with Applications in Medicine

Course Summary
This course will provide an overview of the current ideas in generalized linear mixed models (including generalized linear models) and their manifold medical/health applications with a non-continuous outcome appropriate for analysing studies with simple and more complex hierarchical data structure such as such as nested fixed or random effects.

Course Description
This course will focus on the application of generalized linear and generalized linear mixed models for medical applications with a binary or count outcome. Topics will include simple and more complex hierarchical data structure such as measurements on patients (for example length of stay in hospital) within wards within hospitals, crossed and nested effects, fixed and random effects as well as random coefficient models. The course will give an introduction to the generalized linear model and extend it generalized linear mixed models to cope with potentially nested fixed and random effects simultaneously. All models will be illustrated at hand of study data. The course will include a mixture of lectures and practical workshops using the software STATA.

 

The course is aimed at researchers who want to perform generalized linear model or generalized linear mixed model analysis and/or need to analyse hierarchically structured study data. Participants may be academic researchers in the Medical and Health or Social Sciences sector or may work within the Government, pharmaceutical industry, or other parts of the private sector. 

Participants are expected to have a good working knowledge of simple statistical methods, including a basic understanding of regression and analysis of variance. No familiarity with the software STATA is required. 

Course Code
S3RIMMGLMM2016

Course Dates
12th December 2017 – 13th December 2017 

Course Presenter
Professor Dankmar Böhning