This course is running online, via Zoom.
Mixed models have become increasingly popular, as they have many practical applications. However, the traditional linear mixed model with normally distributed errors may not always be appropriate for modelling discrete response variables, such as binary data and counts. Typically these types of responses are analysed using generalised linear models such as logistic regression and Poisson regression.
Commonly-used generalised linear models will be extended to deal with multiple error structures, using a variety of examples, generally drawn from medical and health related fields. Specific applications, such as repeated measurements and multi-centre trials will also be considered. For example, investigating the presence or absence of adverse events collected in a multi-centre clinical trial.
The emphasis will be on practical understanding, although an outline of the theory will be presented. Practical examples will be used to illustrate the methods, and participants will have the opportunity to fit and interpret models themselves in hands-on computer based practicals.
Note this course does not cover marginal or GEE type models for repeated measurements.
|Session One||Session Two||Q&A with Instructor|
It is assumed that delegates:
Chapter 8: Mixed Models for Binary Data in Collett, D. (2003). Modelling Binary Data. 2nd Edition. Chapman & Hall/CRC.
Verbeke, G. and Molenberghs, G. (2013). Generalized Linear Mixed Models -Overview. In: Scott, M.A., Simonoff, J.S. and Marx, B.D. ed. The SAGE Handbook of Multilevel Modeling. SAGE Publications Ltd, Chapter 8.
Rabe-Hesketh, S. and Skrondal, A. (2012). Multilevel and Longitudinal Modeling Using Stata. Volume 2: Categorical Responses, Counts and Survival. 3rd Edition. Stata Press. (Note this course does not make use of the gllamm Stata program.)
Stroup, W.W. (2013). Generalized Linear Mixed Models. Modern Concepts, Methods and Applications. CRC Press.
The number of attendees is restricted. Please register early to guarantee your place.