Mixed Effects Models for Complex Data

Lang Wu

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November 11, 2009 by Chapman and Hall/CRC
Professional - 431 Pages - 22 B/W Illustrations
ISBN 9781420074024 - CAT# C7402
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

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Features

  • Covers widely used mixed effects models, including LME models, GLMMs, NLME models, frailty models, and semiparametric and nonparametric mixed effects models
  • Links each mixed effects model with the corresponding class of regression model for cross-sectional data
  • Discusses various computational strategies for likelihood estimations of mixed effects models
  • Offers brief descriptions of GEE methods and Bayesian mixed effects models
  • Includes real-world data examples throughout that encompass studies on mental distress, AIDS, and more
  • Explains how to implement standard models using R and S-Plus

Summary

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data.

An overview of general models and methods, along with motivating examples
After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers.

Self-contained coverage of specific topics
Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models.

Background material
In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra.

Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.