Applied Mixed Models in Medicine

Provides a clear understanding of the application of mixed models, and describes the benefits to be gained from their use as well as the practical implications. Mixed models is becoming a popular method of statistical analysis used for analysing medical data, particularly in the pharmaceutical industry. This method often gives improvements over conventional fixed effect models, especially when data are unbalanced. Presently there is no other book covering the application of mixed...
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Provides a clear understanding of the application of mixed models, and describes the benefits to be gained from their use as well as the practical implications. Mixed models is becoming a popular method of statistical analysis used for analysing medical data, particularly in the pharmaceutical industry. This method often gives improvements over conventional fixed effect models, especially when data are unbalanced. Presently there is no other book covering the application of mixed models to clinical data, making this book essential reading for those involved in this subject.

Features include:

* Takes a balanced view of mixed models by discussing some of the problems in their use and indicates where more conventional fixed effect models might be preferred.

* Easily accessible to practitioners in any areas where mixed models are used, including medical statisticians and economists

* Illustrated with numerous medical examples which clearly demonstrate the application of the theory

* Extensive coverage of the underlying theory

* Devotes a complete chapter to the use of software procedures and macros to fit mixed models.

This title is aimed at medical, applied and bio-statisticians, along with teachers and students of advanced statistics courses in mixed models. The book is also suitable for medical scientists who need to understand the techniques used and the assumuptions which underpin their use.