Since the publication of the first edition, the authors have solicited feedback from both instructors who use the book as a text for their courses and researchers who use the book as a resource for their research.  This Second Edition features many additions and revisions based on the feedback of readers.  An additional chapter focusing on linear mixed effects models has been added.  In the first edition, one single chapter was devoted to this topic; however, the coverage has been expanded to two chapters and now includes a detailed discussion and comparison with so-called fixed-effects models, the pros and cons of fixed versus mixed effects models for longitudinal analysis, and a discussion of the simultaneous modeling of cross-sectional and longitudinal effects.  An additional chapter focusing on marginal models and GEE, and again, coverage has been expanded from one chapter to two chapters.  New material for this chapters includes descriptions of models for ordinal responses, discussion of modeling approaches for overdispersed longitudinal data, residuals diagnostics for marginal models focusing primarily on recently developed model-checking techniques based on cumulative sums of residuals over various coordinates, and an introduction to methods for handling missing data and dropout focusing on both imputation and inverse probability weighting methods.  Expanded coverage on generalized linear mixed models addresses penalized quasi-likelihood and marginal quasi-likelihood methods and compares and contrasts these alternative methods of estimation with quadrature-based methods.  A full chapter is now devoted to the design of longitudinal studies to reflect how issues of sample size, power, number of measurements, and duration of study are important for the design of longitudinal studies.  Also new to this book is a chapter focused on the connections between smoothing and linear mixed effects models, especially the links between penalized splines and linear mixed effects models.

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