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A mixed model allows the incorporation of both fixed and randomvariables within a statistical analysis. This enables efficientinferences and more information to be gained from the data. Theapplication of mixed models is an increasingly popular way ofanalysing medical data, particularly in the pharmaceuticalindustry. There have been many recent advances in mixed modelling,particularly regarding the software and applications. This newedition of a groundbreaking text discusses the latest developments,from updated SAS techniques to the increasingly wide range ofapplications.
This much-anticipated second edition is ideal for appliedstatisticians working in medical research and the pharmaceuticalindustry, as well as teachers and students of statistics courses inmixed models. The text will also be of great value to a broad rangeof scientists, particularly those working the medical andpharmaceutical areas.
"synopsis" may belong to another edition of this title.
Helen Brown, Principal Statistician, NHS Scotland, Edinburgh, UK
Robin Prescott, Medical Statistics Unit, University of EdinburghMedical School, UKReview:
"...a valuable mixed model resource for most appliedstatisticians working in the medical environment."(Biometrics, June 2007)
"...useful for practitioners and applied statisticiansworking in medical science." (Journal of the AmericanStatistical Association, September 2007)
"...takes a practical rather than theoretical approach andrequires understanding of only basic statistics." (MAAReviews, October 30, 2006)
“This second edition gives an overview of the theory ofmixed models and its application to real data in medicalresearch.” (Zentralblatt MATH, April 2007)
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