книги по психологии

зарубежная классика

естественные науки

медицина / здравоохранение

зарубежная старинная литература

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.


современная зарубежная литература

Applied Mixed Models in Medicine

Having a baby is an incredible experience, and the ultimate responsibility! Parenting is a job that you start with no training at all – and friends and family always seem to be the first to tell you how best to bring up your children. But there's no sure-fire formula for raising kids. Maybe that's because every child, like every parent, is an individual, and no two parent-child relationships are ever the same. So, you can give up any notions of being a perfect parent. But, you can learn to keep the big mistakes to a minimum and make the parenting experience easier and more rewarding for your children and yourself. Which is where this book comes in. Covering information for newborns to pre-teens, Parenting For Dummies gives you the essentials of parenting basics. From dealing with a crying baby and potty training, to building self-esteem and dealing with sibling rivalry, it offers a gold mine of up-to-date advice.

биографии и мемуары

православное христианство

письма

жизненный путь

христианская жизнь

религиозные деятели

воспоминания

православие

служение людям

Parenting For Dummies

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. 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. 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 new version of SAS, including the procedure PROC GLIMMIX and an introduction to other available software. Supported by a website featuring computer code, data sets, and further material, available at: http://www.chs.med.ed.ac.uk/phs/mixed/. 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.

Helen Brown