Linear Mixed-Effects Models Using R A Step-by-Step Approach /

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 wid...

Full description

Main Authors: Gałecki, Andrzej. (Author, http://id.loc.gov/vocabulary/relators/aut), Burzykowski, Tomasz. (http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2013.
Edition:1st ed. 2013.
Series:Springer Texts in Statistics,
Subjects:
Online Access:https://doi.org/10.1007/978-1-4614-3900-4
LEADER 05812nam a22005055i 4500
001 978-1-4614-3900-4
003 DE-He213
005 20210619000553.0
007 cr nn 008mamaa
008 130217s2013 xxu| s |||| 0|eng d
020 |a 9781461439004  |9 978-1-4614-3900-4 
024 7 |a 10.1007/978-1-4614-3900-4  |2 doi 
050 4 |a QA276-280 
072 7 |a PBT  |2 bicssc 
072 7 |a MAT029000  |2 bisacsh 
072 7 |a PBT  |2 thema 
082 0 4 |a 519.5  |2 23 
100 1 |a Gałecki, Andrzej.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a Linear Mixed-Effects Models Using R  |h [electronic resource] :  |b A Step-by-Step Approach /  |c by Andrzej Gałecki, Tomasz Burzykowski. 
250 |a 1st ed. 2013. 
264 1 |a New York, NY :  |b Springer New York :  |b Imprint: Springer,  |c 2013. 
300 |a XXXII, 542 p. 64 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Springer Texts in Statistics,  |x 1431-875X 
505 0 |a Introduction -- Linear Models for Independent Observations -- Linear Fixed-effects Models for Correlated Data -- Linear Mixed-effects Models. 
520 |a 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. Andrzej Gałecki is a Research Professor in the Division of Geriatric Medicine, Department of Internal Medicine, and Institute of Gerontology at the University of Michigan Medical School, and is Research Scientist in the Department of Biostatistics at the University of Michigan School of Public Health. He earned his M.Sc. in applied mathematics (1977) from the Technical University of Warsaw, Poland, and an M.D. (1981) from the Medical University of Warsaw. In 1985 he earned a Ph.D. in epidemiology from the Institute of Mother and Child Care in Warsaw (Poland). He is a member of the Editorial Board of the Open Journal of Applied Sciences. Since 1990, Dr. Galecki has collaborated with researchers in gerontology and geriatrics. His research interests lie in the development and application of statistical methods for analyzing correlated and over- dispersed data. He developed the SAS macro NLMEM for nonlinear mixed-effects models, specified as a solution to ordinary differential equations. He also proposed a general class of variance-covariance structures for the analysis of multiple continuous dependent variables measured over time. This methodology is considered to be one of first approaches to joint models for longitudinal data.   Tomasz Burzykowski is Professor of Biostatistics and Bioinformatics at Hasselt University (Belgium) and Vice-President of Research at the International Drug Development Institute (IDDI) in Louvain-la-Neuve (Belgium). He received the M.Sc. degree in applied mathematics (1990) from Warsaw University, and the M.Sc. (1991) and Ph.D. (2001) degrees from Hasselt University. He has held guest professorships at the Karolinska Institute (Sweden), the Medical University of Bialystok (Poland), and the Technical University of Warsaw (Poland). He serves as Associate Editor of Biometrics. Dr. Burzykowski published methodological work on survival analysis, meta-analyses of clinical trials, validation of surrogate endpoints, analysis of gene expression data, and modelling of peptide-centric mass-spectrometry data. He is also a co-author of numerous papers applying statistical methods to clinical data in different disease areas. 
650 0 |a Statistics . 
650 1 4 |a Statistical Theory and Methods.  |0 https://scigraph.springernature.com/ontologies/product-market-codes/S11001 
650 2 4 |a Statistics, general.  |0 https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 
650 2 4 |a Statistics and Computing/Statistics Programs.  |0 https://scigraph.springernature.com/ontologies/product-market-codes/S12008 
700 1 |a Burzykowski, Tomasz.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9781489996671 
776 0 8 |i Printed edition:  |z 9781461438991 
776 0 8 |i Printed edition:  |z 9781461439011 
830 0 |a Springer Texts in Statistics,  |x 1431-875X 
856 4 0 |u https://doi.org/10.1007/978-1-4614-3900-4 
912 |a ZDB-2-SMA 
912 |a ZDB-2-SXMS 
950 |a Mathematics and Statistics (SpringerNature-11649) 
950 |a Mathematics and Statistics (R0) (SpringerNature-43713)