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 naive methods are used and shows which approaches should
be used to achieve accurate results in longitudinal data
analysis.
General
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