Fee Download Linear Mixed Models for Longitudinal Data (Springer Series in Statistics), by Geert Verbeke, Geert Molenberghs
Based on the Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs specifics that we offer, you could not be so baffled to be below and also to be member. Get now the soft documents of this book Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs as well as wait to be all yours. You saving could lead you to stimulate the convenience of you in reading this book Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs Even this is forms of soft documents. You could truly make better possibility to obtain this Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs as the advised book to check out.
Linear Mixed Models for Longitudinal Data (Springer Series in Statistics), by Geert Verbeke, Geert Molenberghs
Fee Download Linear Mixed Models for Longitudinal Data (Springer Series in Statistics), by Geert Verbeke, Geert Molenberghs
Picture that you get such particular remarkable encounter as well as knowledge by simply checking out an e-book Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs. Exactly how can? It seems to be greater when an e-book could be the best point to discover. Publications now will certainly appear in printed as well as soft data collection. Among them is this e-book Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs It is so common with the published publications. Nonetheless, many individuals occasionally have no space to bring the book for them; this is why they cannot read guide any place they desire.
For everyone, if you wish to start accompanying others to check out a book, this Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs is much recommended. As well as you need to obtain the book Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs right here, in the link download that we give. Why should be below? If you desire various other kind of books, you will certainly consistently discover them as well as Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs Economics, national politics, social, scientific researches, faiths, Fictions, and also a lot more books are supplied. These readily available books remain in the soft data.
Why should soft data? As this Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs, many people likewise will certainly need to purchase guide quicker. Yet, occasionally it's up until now means to get guide Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs, also in other nation or city. So, to ease you in locating guides Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs that will sustain you, we help you by providing the lists. It's not only the list. We will offer the recommended book Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs web link that can be downloaded and install straight. So, it will not need more times as well as days to posture it as well as other books.
Gather guide Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs begin with now. Yet the new way is by accumulating the soft data of guide Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs Taking the soft data can be saved or kept in computer system or in your laptop computer. So, it can be greater than a book Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs that you have. The simplest way to expose is that you could likewise conserve the soft documents of Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs in your appropriate and also available device. This condition will mean you too often read Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs in the spare times greater than chatting or gossiping. It will certainly not make you have bad habit, yet it will lead you to have much better habit to read book Linear Mixed Models For Longitudinal Data (Springer Series In Statistics), By Geert Verbeke, Geert Molenberghs.
This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place.
Most analyses were done with the MIXED procedure of the SAS software package, but the data analyses are presented in a software-independent fashion.
- Sales Rank: #1796843 in Books
- Published on: 2008-10-10
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.34" w x 6.10" l, 1.80 pounds
- Binding: Paperback
- 570 pages
Review
From the reviews:
MATHEMATICAL REVIEWS
"This book emphasizes practice rather than mathematical rigor and the majority of the chapters are explanatory rather than research oriented. In this respect, guidance and advice on practical issues are the main focus of the text. Hence it will be of interest to applied statisticians and biomedical researchers in industry, particularly in the pharmaceutical industry, medical public health organizations, contract research organizations, and academia."
"This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Over 125 illustrations are included in the book. … I do believe that the book may serve as a useful reference to a broader audience. Since practical examples are provided as well as discussion of the leading software utilization, it may also be appropriate as a textbook in an advanced undergraduate-level or a graduate-level course in an applied statistics program." (Ana Ivelisse Avil �s, Technometrics, Vol. 43 (3), 2001)
"A practical book with a great many examples, including worked computer code and access to the datasets. … The authors state that the book covers ‘linear mixed models for continuous outcomes’ … . The book has four main strengths: its practical bent, its emphasis on exploratory analysis, its description of tools for model checking, and its treatment of dropout and missingness … . my impression of the book was … positive. Its strong practical nature and emphasis on dropout modelling are particularly welcome … ." (Harry Southworth, ISCB Newsletter, June, 2002)
"This book is devoted to linear mixed-effects models with strong emphasis on the SAS procedure. Guidance and advice on practical issues are the main focus of the text. … It is of value to applied statisticians and biomedical researchers. … I recommend this book as a reference to applied statisticians and biomedical researchers, particularly in the pharmaceutical industry, medical and public organizations." (Wang Songgui, Zentralblatt MATH, Vol. 956, 2001)
From the Back Cover
This paperback edition is a reprint of the 2000 edition.
This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Several variations to the conventional linear mixed model are discussed (a heterogeity model, conditional linear mixed models). This book will be of interest to applied statisticians and biomedical researchers in industry, public health organizations, contract research organizations, and academia. The book is explanatory rather than mathematically rigorous. Most analyses were done with the MIXED procedure of the SAS software package, and many of its features are clearly elucidated. However, some other commercially available packages are discussed as well. Great care has been taken in presenting the data analyses in a software-independent fashion.
Geert Verbeke is Professor in Biostatistics at the Biostatistical Centre of the Katholieke Universiteit Leuven in Belgium. He is Past President of the Belgian Region of the International Biometric Society, a Board Member of the American Statistical Association, and past Joint Editor of the Journal of the Royal Statistical Society, Series A (2005--2008). He is the director of the Leuven Center for Biostatistics and statistical Bioinformatics (L-BioStat), and vice-director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), a joint initiative of the Hasselt and Leuven universities in Belgium.
Geert Molenberghs is Professor of Biostatistics at Universiteit Hasselt and Katholieke Universiteit Leuven in Belgium. He was Joint Editor of Applied Statistics (2001-2004) and Co-Editor of Biometrics (2007-2009). He was President of the International Biometric Society (2004-2005), and has received the Guy Medal in Bronze from the Royal Statistical Society and the Myrto Lefkopoulou award from the Harvard School of Public Health. He is founding director of the Center for Statistics and also the director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics.
Both authors have received the American Statistical Association's Excellence in Continuing Education Award in 2002, 2004, 2005, and 2008. Both are elected Fellows of the American Statistical Association and elected members of the International Statistical Institute.
About the Author
Geert Verbeke is Assistant Professor at the Biostistical Centre of the Katholieke Universiteit Leuven in Belgium. He received the B.S. degree in mathematics (1989) from the Katholieke Universiteit Leuven, the M.S. in biostatistics (1992) from the Limburgs Universitair Centrum, and earned a Ph.D. in biostatistics (1995) from the Katholieke Universiteit Leuven. Dr. Verbeke wrote his dissertation, as well as a number of methodological articles, on various aspects of linear mixed models for longitudinal data analysis. He has held visiting positions at the Gerontology Research Center and the Johns Hopkins University. Geert Molenberghs is Assistant Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium.
Most helpful customer reviews
27 of 28 people found the following review helpful.
excellent for applications to clinical trials data with some missing data
By Michael R. Chernick
This book is basically an update of their 1997 mongraph. Longitudinal data are important in biostatistics and particularly in the analysis of clinical trials. There are effective methods for handling longitudinal data using linear models with covariance structures that represent the time dependence of the repeated observations. There are many subtle issues in the analysis and many who analyze longitudinal data apply incorrect linear models and are often not aware of the consequences of their decisions. The authors were motivated to provide a reference source to remedy this problem. The book presents the theory and applications and uses SAS Proc Mixed as a vehicle for presenting many of the results in a clear and understandable fashion. An important feature of the book is its emphasis on how best to deal with the problem of missing data. This is covered in chapters 14 - 16. Although SAS is emphasized throughout the book other software tools are also illustrated in Appendix A (including SPlus). SUDAAN is a package produced by the Research Triangle Institute in North Carolina that also handles longitudinal data but is overlooked by the authors. Another great book on longitudinal data analysis is Diggle, Liang and Zeger "Analysis of Longitudinal Data" published in 1994. There have been many advances since 1994 and Verbeke and Molenberghs cover a great deal of it. You can find my review of Diggle, Liang and Zeger on Amazon. An updated second edition of their book has now appeared and is more up-to-date. I find this book by Verbeke and Molenberghs one of the best and most innovative on this topic. Another nice addition is the new book on missing data in clinical studies by Molenberghs and Kennard. I have written an amazon trview on that one also.
6 of 8 people found the following review helpful.
Excellent book
By Savvas Papadopoulos
The book covers many advanced topics of Longitudinal data with many examples and SAS programs. Congatulations to the authors for this outstanding job.
Savas Papadopoulos
2 of 4 people found the following review helpful.
Poor book, better books are available
By Madan Gopal Kundu
This book is too much practical, no discussion of theory at all. Should not be recommended for any graduate course. May be useful for some applied purpose, but in that case books such as Applied Longitudinal data analysis by Fitzmaurice will serve better the purpose.
I don't recommend this book at all.
Linear Mixed Models for Longitudinal Data (Springer Series in Statistics), by Geert Verbeke, Geert Molenberghs PDF
Linear Mixed Models for Longitudinal Data (Springer Series in Statistics), by Geert Verbeke, Geert Molenberghs EPub
Linear Mixed Models for Longitudinal Data (Springer Series in Statistics), by Geert Verbeke, Geert Molenberghs Doc
Linear Mixed Models for Longitudinal Data (Springer Series in Statistics), by Geert Verbeke, Geert Molenberghs iBooks
Linear Mixed Models for Longitudinal Data (Springer Series in Statistics), by Geert Verbeke, Geert Molenberghs rtf
Linear Mixed Models for Longitudinal Data (Springer Series in Statistics), by Geert Verbeke, Geert Molenberghs Mobipocket
Linear Mixed Models for Longitudinal Data (Springer Series in Statistics), by Geert Verbeke, Geert Molenberghs Kindle