Linear Mixed and Generalized Linear Mixed Models with Applications in Medicine 2018

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

Individual day(s) are available on request, please email ProfessionalTraining.FSHS@soton.ac.uk , who will happy to advise. 

Course Code S3RILMGLMM2018
Course Dates 12th December 2018 – 14th December 2018
Places Available 25
Course Leader Professor Dankmar Boehning & Dr Antonello Maruotti

Course Description
This course will focus on the application of linear mixed models for medical applications with a continuous outcome as well as a binary or count outcome. Topics will include simple and more complex hierarchical data structure such as repeated measurements on patients 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 general mixed model and highlight its ability to cope with potentially nested fixed and random effects simultaneously. Data structures with repeated measures in time will also be touched upon. 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 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.