Linear Mixed Models: A Practical Guide Using Statistical Software

Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-navigate reference details the use of procedures for fitting LMMs in five popular statistical software packages: SAS, SPSS, Stata, R/S-plus, and HLM. The authors introduce basic theoretical concepts, present a heuristic approach to fitting LMMs based on both general and hierarchical model specifications, develop the model-building process step-by-step, and demonstrate the estimation, testing, and interpretation of fixed-effect parameters and covariance parameters associated with random effects. These concepts are illustrated through examples using real-world data sets that enable comparisons of model fitting options and results across the software procedures. The book also gives an overview of important options and features available in each procedure. Making popular software procedures for fitting LMMs easy-to-use, this valuable resource shows how to perform LMM analyses and provides a clear explanation of mixed modeling techniques and theories.

Table of Contents:
PREFACE INTRODUCTION What Are Linear Mixed Models (LMMs)? A Brief History of Linear Mixed Models LINEAR MIXED MODELS: AN OVERVIEW Introduction Specification of LMMs The Marginal Linear Model Estimation in LMMs Computational Issues Tools for Model Selection Model Building Strategies Checking Model Assumptions (Diagnostics) Other Aspects of LMMs Chapter Summary TWO-LEVEL MODELS FOR CLUSTERED DATA: THE RAT PUP EXAMPLE Introduction The Rat Pup Study Overview of the Rat Pup Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model Estimating the Intraclass Correlation Coefficients (ICCs) Calculating Predicted Values Diagnostics for the Final Model Software Notes THREE-LEVEL MODELS FOR CLUSTERED DATA; THE CLASSROOM EXAMPLE Introduction The Classroom Study Overview of the Classroom Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model Estimating the Intraclass Correlation Coefficients (ICCs) Calculating Predicted Values Diagnostics for the Final Model Software Notes MODELS FOR REPEATED MEASURES DATA: THE RAT BRAIN EXAMPLE Introduction The Rat Brain Study Overview of the Rat Brain Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model The Implied Marginal Variance-Covariance Matrix for the Final Model Diagnostics for the Final Model Software Notes Other Analytic Approaches RANDOM COEFFICIENT MODELS FOR LONGITUDINAL DATA: THE AUTISM EXAMPLE Introduction The Autism Study Overview of the Autism Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model Calculating Predicted Values Diagnostics for the Final Model Software Note: Computational Problems with the D Matrix An Alternative Approach: Fitting the Marginal Model with an Unstructured Covariance Matrix MODELS FOR CLUSTERED LONGITUDINAL DATA: THE DENTAL VENEER EXAMPLE Introduction The Dental Veneer Study Overview of the Dental Veneer Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model The Implied Marginal Variance-Covariance Matrix for the Final Model Diagnostics for the Final Model Software Notes Other Analytic Approaches REFERENCES APPENDIX A: STATISTICAL SOFTWARE RESOURCES APPENDIX B: CALCULATION OF THE MARGINAL VARIANCE-COVARIANCE MATRIX APPENDIX C: ACRONYMS/ABBREVIATIONS INDEX

Review :
In this book the authors take on the herculean task of demonstrating how to perform complex LMM analyses with five programs: HLM, R/S--PLUS, SAS, SPSS, and Stata. It is much more than a software manual; through the use of excellent introductory material and details given throughout, it provides a solid introduction to LMM analysis. Software-oriented books can quickly become dated, but the book's website http://www-personal.umich.edu/~bwest/almmussp.html appears to keep up with new developments, this book is a tremendous contribution to the field of applied mixed modeling. It is much more than a software manual. It is well organized, has minimal typographical errors, and contains a complete index. It could easily serve as a reference guide. Anyone working with LMMs should seriously consider obtaining this book. --Technometrics, May 2009, Vol. 51, No. 2, the book is very useful for the well-informed practitioner, who wants to fit LMMs and needs to make a choice about the specific statistical software to use. --Biometrical Journal, Vol. 51, 2009, useful to someone who wanted to understand the process of setting up, conducting, and evaluating a mixed model analysis. West et al, is appropriate for a researcher with problems where model selection is a major component of the analysis. ... would also be useful to a practicing statistician who is familiar with mixed models and wishes to use a new statistical package. --Biometrics, December 2008 This text is a most welcome addition to the literature on regression models. It is one of those rare texts with no glaring omissions or obvious shortcomings. The book consolidates syntax germane to LMMs for most major software packages, obviating the need to consult multiple platform-specific texts. Most importantly, the material is presented in an easy-to-read, sensibly organized fashion, a must buy for the applied statistician and researcher alike. --Gregory E. Gilbert, Medical University of South Carolina, Charleston, USA, Journal of the American Statistical Association I commend this book to anyone who is using software for statistical modelling, either for a detailed account of specific linear models or an exemplar of how to gather the information to compare software. --Journal of the Royal Statistical Society, a good reference for any practicing statisticians and researchers who want a basic introduction to the topic ... also useful for researchers who need to compare their analysis to existing works done using different software packages. Because the basic concept is well summarized and presented through examples and tables ... I would recommend this textbook as a special topic for teaching an advanced undergraduate or introductory graduate course on linear models. --Journal of Quality Technology ... an excellent first course in the theory and methods of linear mixed models, also provides a thorough and up-to-date guide through the major software applications for linear mixed models, namely, Stata, SAS, R, SPSS, and HLM. Each of five middle chapters highlights a different software package and teaches you the basics of fitting mixed models therein. Tables comparing each package show the results obtained from fitting identical models, ... . If you wish to fit linear mixed models, whether in Stata or elsewhere, we recommend this text. --Stata Technical Group