Longitudinal Data Analysis

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.