<p><i>Methods and Applications of Longitudinal Data Analysis</i> describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include:</p><ul><li>descriptive methods for delineating trends over time</li><li>linear mixed regression models with both fixed and random effects</li><li>covariance pattern models on correlated errors</li><li>generalized estimating equations</li><li>nonlinear regression models for categorical repeated measurements</li><li>techniques for analyzing longitudinal data with non-ignorable missing observations</li></ul><p>Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data.</p><p><i>Methods and Applications of Longitudinal Data Analysis</i> equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists.</p><p><ul><li>From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis</li><li>Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection</li><li>Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.</li></ul>
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