Items related to Longitudinal Data Analysis (Wiley Series in Probability...
Synopsis
This book presents and describes methods for analysis of longitudinal data, with a strong emphasis on the application of these methods to problems in the biomedical and behavioral sciences. This is an important book because longitudinal data are increasingly common in many areas of research, and methods of analysis of such data are not well understood by data analysts. Therefore, the book is geared more toward users, and not developers, of statistics. The Second Edition features six new chapters on: Bivariate and Multivariate Models; Growth Mixture Models; Grouped and Discrete Time Survival Analysis Models; Mixed-effects Regression Models for Higher-Level Data; Intensive Longitudinal Data; and Sample Size and Power Determination in Longitudinal Studies. In addition, the introductory chapter now describes the pertinent features of each presented dataset in an effort to streamline the presentation of the datasets. The relevant variables of the datasets and the scientific aim of the studies are described. Specific statistical procedures covered within the book include: repeated measures analysis of variance, multivariate analysis of variance for repeated measures, random-effects regression models (RRM), covariance-structure models, generalized-estimating equations (GEE) models, and generalizations of RRM and GE for categorical outcomes. This book emphasizes methods for analysis of longitudinal data analysis, which are extensively illustrated using real examples. The authors have chosen not to focus on software in the book, though some syntax examples are provided. Many programs are available for the analyses presented in this book including SAS, SPSS, SYSTAT, HLM, MLwiN, MIXREG/MIXOR and Mplus. Several datasets and computer syntax examples are posted on the book's related website. It is the aim of the authors to keep these syntax examples current as new versions of the software programs emerge.
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From the Back Cover
Longitudinal data analysis for biomedical and behavioral sciences
This innovative book sets forth and describes methods for the analysis of longitudinaldata, emphasizing applications to problems in the biomedical and behavioral sciences. Reflecting the growing importance and use of longitudinal data across many areas of research, the text is designed to help users of statistics better analyze and understand this type of data.
Much of the material from the book grew out of a course taught by Dr. Hedeker on longitudinal data analysis. The material is, therefore, thoroughly classroom tested and includes a number of features designed to help readers better understand and apply the material. Statistical procedures featured within the text include:
- Repeated measures analysis of variance
- Multivariate analysis of variance for repeated measures
- Random-effects regression models (RRM)
- Covariance-pattern models
- Generalized-estimating equations (GEE) models
- Generalizations of RRM and GEE for categorical outcomes
Practical in their approach, the authors emphasize the applications of the methods, using real-world examples for illustration. Some syntax examples are provided, although the authors do not generally focus on software in this book. Several datasets and computer syntax examples are posted on this title's companion Web site. The authors intend to keep the syntax examples current as new versions of the software programs emerge.
This text is designed for both undergraduate and graduate courses in longitudinal data analysis. Instructors can take advantage of overheads and additional course materials available online for adopters. Applied statisticians in biomedicine and the social sciences can also use the book as a convenient reference.
About the Author
DONALD HEDEKER, PHD, is Professor of Biostatistics in the Division of Epidemiology and Biostatistics, School of Public Health at the University of Illinois at Chicago. He is a Fellow of the American Statistical Association and the author of numerous peer-reviewed papers.
ROBERT D. GIBBONS, PHD, is Director of the Center for Health Statistics; Professor of Biostatistics in the Division of Epidemiology and Biostatistics, School of Public Health; and Professor of Psychiatry in the College of Medicine, all at the University of Illinois at Chicago. He is a Fellow of the American Statistical Association and the author of numerous peer-reviewed papers.
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