A mixed model allows the incorporation of both fixed and random variables within a statistical analysis. This enables efficient inferences and more information to be gained from the data. The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This new edition of a groundbreaking text discusses the latest developments, from updated SAS techniques to the increasingly wide range of applications. 
<ul>
<li>Presents an overview of the theory and applications of mixed models in medical research, including the latest developments and new sections on bioequivalence, cluster randomised trials and missing data.</li> <li>Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists.</li> <li>Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output.</li> <li>Features new version of SAS, including the procedure PROC GLIMMIX and an introduction to other available software.</li> <li>Supported by a website featuring computer code, data sets, and further material, available at: http://www.chs.med.ed.ac.uk/phs/mixed/.</li> </ul>
This much-anticipated second edition is ideal for applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The text will also be of great value to a broad range of scientists, particularly those working the medical and pharmaceutical areas.

Applied Mixed Models in Medicine

Science & Math

Brown, Helen

Prescott, Robin

Biological Sciences

wiley

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.

John Wiley&Sons, Inc.

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. <ul>
<li>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.</li> <li>Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists.</li> <li>Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output.</li> <li>Features the new version of SAS, including new graphics for model diagnostics and the procedure PROC MCMC.</li> <li>Supported by a website featuring computer code, data sets, and further material. </li> </ul>
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.

Applied Mixed Models in Medicine (Statistics in Practice)

See the best price to sell, buy, or rent books by Prescott, Robin. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Prescott, Robin

See the best price to sell, buy, or rent books by Prescott, Robin. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Applied Mixed Models in Medicine

Applied Mixed Models in Medicine

Applied Mixed Models in Medicine (Statistics in Practice)