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Applied Mixed Models In Medicine


Applied Mixed Models In Medicine
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Applied Mixed Models In Medicine


Applied Mixed Models In Medicine
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Author : Helen Brown
language : en
Publisher: John Wiley & Sons
Release Date : 2015-02-16

Applied Mixed Models In Medicine written by Helen Brown and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-16 with Medical categories.


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.



Applied Mixed Models In Medicine


Applied Mixed Models In Medicine
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Author : Helen Brown
language : en
Publisher: John Wiley & Sons
Release Date : 2014-12-12

Applied Mixed Models In Medicine written by Helen Brown and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-12 with Medical categories.


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 wayof analysing medical data, particularly in the pharmaceuticalindustry. A mixed model allows the incorporation of both fixed andrandom variables within a statistical analysis, enabling efficientinferences and more information to be gained from the data. Therehave been many recent advances in mixed modelling, particularlyregarding the software and applications. This third edition ofBrown and Prescott’s groundbreaking text provides an updateon the latest developments, and includes guidance on the use ofcurrent SAS techniques across a wide range of applications. Presents an overview of the theory and applications of mixedmodels in medical research, including the latest developments andnew sections on incomplete block designs and the analysis ofbilateral data. Easily accessible to practitioners in any area where mixedmodels are used, including medical statisticians andeconomists. Includes numerous examples using real data from medical andhealth research, and epidemiology, illustrated with SAS code andoutput. Features the new version of SAS, including new graphics formodel diagnostics and the procedure PROC MCMC. Supported by a website featuring computer code, data sets, andfurther material. This third edition will appeal to applied statisticians workingin medical research and the pharmaceutical industry, as well asteachers and students of statistics courses in mixed models. Thebook will also be of great value to a broad range of scientists,particularly those working in the medical and pharmaceuticalareas.



Applied Mixed Models In Medicine


Applied Mixed Models In Medicine
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Author : Kate Brown
language : en
Publisher: Halsted Press
Release Date : 2003-05-01

Applied Mixed Models In Medicine written by Kate Brown and has been published by Halsted Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-05-01 with Medical categories.




Linear Mixed Effects Models Using R


Linear Mixed Effects Models Using R
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Author : Andrzej Gałecki
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-02-05

Linear Mixed Effects Models Using R written by Andrzej Gałecki and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-05 with Mathematics categories.


Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied researchers in those and other fields by providing state-of-the-art descriptions of the implementation of LMMs in R. To help readers to get familiar with the features of the models and the details of carrying them out in R, the book includes a review of the most important theoretical concepts of the models. The presentation connects theory, software and applications. It is built up incrementally, starting with a summary of the concepts underlying simpler classes of linear models like the classical regression model, and carrying them forward to LMMs. A similar step-by-step approach is used to describe the R tools for LMMs. All the classes of linear models presented in the book are illustrated using real-life data. The book also introduces several novel R tools for LMMs, including new class of variance-covariance structure for random-effects, methods for influence diagnostics and for power calculations. They are included into an R package that should assist the readers in applying these and other methods presented in this text.



Mixed Models


Mixed Models
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Author : Eugene Demidenko
language : en
Publisher: John Wiley & Sons
Release Date : 2013-08-05

Mixed Models written by Eugene Demidenko and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-05 with Mathematics categories.


Praise for the First Edition “This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one’s personal library.” —Journal of the American Statistical Association Mixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. Mixed Models: Theory and Applications with R, Second Edition fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R. The new edition provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing. Mixed Models: Theory and Applications with R, Second Edition features unique applications of mixed model methodology, as well as: Comprehensive theoretical discussions illustrated by examples and figures Over 300 exercises, end-of-section problems, updated data sets, and R subroutines Problems and extended projects requiring simulations in R intended to reinforce material Summaries of major results and general points of discussion at the end of each chapter Open problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations Ideal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.



Linear And Generalized Linear Mixed Models And Their Applications


Linear And Generalized Linear Mixed Models And Their Applications
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Author : Jiming Jiang
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-30

Linear And Generalized Linear Mixed Models And Their Applications written by Jiming Jiang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-30 with Mathematics categories.


Over the past decade there has been an explosion of developments in mixed e?ects models and their applications. This book concentrates on two major classes of mixed e?ects models, linear mixed models and generalized linear mixed models, with the intention of o?ering an up-to-date account of theory and methods in the analysis of these models as well as their applications in various ?elds. The ?rst two chapters are devoted to linear mixed models. We classify l- ear mixed models as Gaussian (linear) mixed models and non-Gaussian linear mixed models. There have been extensive studies in estimation in Gaussian mixed models as well as tests and con?dence intervals. On the other hand, the literature on non-Gaussian linear mixed models is much less extensive, partially because of the di?culties in inference about these models. However, non-Gaussian linear mixed models are important because, in practice, one is never certain that normality holds. This book o?ers a systematic approach to inference about non-Gaussian linear mixed models. In particular, it has included recently developed methods, such as partially observed information, iterative weighted least squares, and jackknife in the context of mixed models. Other new methods introduced in this book include goodness-of-?t tests, p- diction intervals, and mixed model selection. These are, of course, in addition to traditional topics such as maximum likelihood and restricted maximum likelihood in Gaussian mixed models.



Linear Mixed Models


Linear Mixed Models
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Author : Brady T. West
language : en
Publisher: CRC Press
Release Date : 2014-07-17

Linear Mixed Models written by Brady T. West and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-17 with Mathematics categories.


Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields. It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM. New to the Second Edition A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggested approaches to writing simulations Use of the lmer() function in the lme4 R package New sections on fitting LMMs to complex sample survey data and Bayesian approaches to making inferences based on LMMs Updated graphical procedures in the software packages Substantially revised index to enable more efficient reading and easier location of material on selected topics or software options More practical recommendations on using the software for analysis A new R package (WWGbook) that contains all of the data sets used in the examples Ideal for anyone who uses software for statistical modeling, this book eliminates the need to read multiple software-specific texts by covering the most popular software programs for fitting LMMs in one handy guide. The authors illustrate the models and methods through real-world examples that enable comparisons of model-fitting options and results across the software procedures.



Applied Mixed Model Analysis


Applied Mixed Model Analysis
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Author : Jos W. R. Twisk
language : en
Publisher: Cambridge University Press
Release Date : 2019-04-18

Applied Mixed Model Analysis written by Jos W. R. Twisk and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-18 with Mathematics categories.


Emphasizing interpretation of results, this hands-on guide explains why, when, and how to use mixed models with your data.



Sas For Mixed Models


Sas For Mixed Models
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Author : Walter W. Stroup
language : en
Publisher: SAS Institute
Release Date : 2018-12-12

Sas For Mixed Models written by Walter W. Stroup and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-12 with Computers categories.


Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models This book is part of the SAS Press program.



Regression Models As A Tool In Medical Research


Regression Models As A Tool In Medical Research
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Author : Werner Vach
language : en
Publisher: CRC Press
Release Date : 2012-11-27

Regression Models As A Tool In Medical Research written by Werner Vach and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-27 with Mathematics categories.


While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the results is often challenging for beginners. This text presents the fundamental concepts and important aspects of regression models most commonly used in medical research, including the classical regression model for continuous outcomes, the logistic regression model for binary outcomes, and the Cox proportional hazards model for survival data. The author emphasizes adequate use, correct interpretation of results, appropriate presentation of results, and avoidance of potential pitfalls.