Applied Mixed Models in Medicine

A fully updated edition of this key text on mixed models, focusing on applications in medical research 

The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott's groundbreaking text provides an update on the latest developments, and includes guidance on the use of current SAS techniques across a wide range of applications. 

• Presents an overview of the theory and applications of mixed models in medical research, including the latest developments and new sections on incomplete block designs and the analysis of bilateral data.
• Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists.
• Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output.
• Features the new version of SAS, including new graphics for model diagnostics and the procedure PROC MCMC.
• Supported by a website featuring computer code, data sets, and further material. 

This third edition will appeal to applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The book will also be of great value to a broad range of scientists, particularly those working in the medical and pharmaceutical areas.

Helen Brown, Principal Statistician, NHS Scotland, Edinburgh, UK.

Robin Prescott, Medical Statistics Unit, University of Edinburgh Medical School, UK.