Methods and Applications of Longitudinal Data Analysis

✍ Scribed by Liu, Xian

• Publisher: Academic Press is an imprint of Elsevier
• Year: 2015
• Tongue: English
• Leaves: 507
• Edition: 1
• Category: Library

✦ Synopsis

Methods and Applications of Longitudinal Data Analysis 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:

• descriptive methods for delineating trends over time
• linear mixed regression models with both fixed and random effects
• covariance pattern models on correlated errors
• generalized estimating equations
• nonlinear regression models for categorical repeated measurements
• techniques for analyzing longitudinal data with non-ignorable missing observations

Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data.

Methods and Applications of Longitudinal Data Analysis 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.

• 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
• Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection
• Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.

✦ Table of Contents

Content:
Front matter,Copyright,Biography,PrefaceEntitled to full textChapter 1 - Introduction, Pages 1-18
Chapter 2 - Traditional methods of longitudinal data analysis, Pages 19-59
Chapter 3 - Linear mixed-effects models, Pages 61-94
Chapter 4 - Restricted maximum likelihood and inference of random effects in linear mixed models, Pages 95-131
Chapter 5 - Patterns of residual covariance structure, Pages 133-171
Chapter 6 - Residual and influence diagnostics, Pages 173-203
Chapter 7 - Special topics on linear mixed models, Pages 205-242
Chapter 8 - Generalized linear mixed models on nonlinear longitudinal data, Pages 243-279
Chapter 9 - Generalized estimating equations (GEEs) models, Pages 281-308
Chapter 10 - Mixed-effects regression model for binary longitudinal data, Pages 309-341
Chapter 11 - Mixed-effects multinomial logit model for nominal outcomes, Pages 343-378
Chapter 12 - Longitudinal transition models for categorical response data, Pages 379-410
Chapter 13 - Latent growth, latent growth mixture, and group-based models, Pages 411-440
Chapter 14 - Methods for handling missing data, Pages 441-473
Appendix A - Orthogonal polynomials, Pages 475-476
Appendix B - The delta method, Pages 477-478
Appendix C - Quasi-likelihood functions and properties, Pages 479-481
Appendix D - Model specification and SAS program for random coefficient multinomial logit model on health state among older Americans, Pages 483-485
References, Pages 487-498
Subject Index, Pages 499-511