Wu Mixed Effects Models for Complex Data

Erscheinungsjahr 2010
ISBN: 978-1-4200-7408-6
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 431 Seiten

Reihe: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

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.

Zielgruppe

Researchers and graduate students in statistics and biostatistics; quantitative researchers in medicine, epidemiology, pharmaceutical science, and social science.

Autoren/Hrsg.

Wu, Lang

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Naturwissenschaften Biowissenschaften Biowissenschaften
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

Introduction
Introduction

Longitudinal Data and Clustered Data

Some Examples

Regression Models

Mixed Effects Models
Complex or Incomplete Data

Software

Outline and Notation
Mixed Effects Models
Introduction

Linear Mixed Effects (LME) Models

Nonlinear Mixed Effects (NLME) Models

Generalized Linear Mixed Models (GLMMs)

Nonparametric and Semiparametric Mixed Effects Models

Computational Strategies

Further Topics

Software
Missing Data, Measurement Errors, and Outliers
Introduction

Missing Data Mechanisms and Ignorability

General Methods for Missing Data
EM Algorithms

Multiple Imputation

General Methods for Measurement Errors

General Methods for Outliers

Software
Mixed Effects Models with Missing Data
Introduction

Mixed Effects Models with Missing Covariates

Approximate Methods
Mixed Effects Models with Missing Responses

Multiple Imputation Methods

Computational Strategies

Examples
Mixed Effects Models with Covariate Measurement Errors
Introduction

Measurement Error Models and Methods

Two-Step Methods and Regression Calibration Methods

Likelihood Methods

Approximate Methods
Measurement Error and Missing Data
Mixed Effects Models with Censoring

Introduction

Mixed Effects Models with Censored Responses

Mixed Effects Models with Censoring and Measurement Errors

Mixed Effects Models with Censoring and Missing Data

Appendix
Survival Mixed Effects (Frailty) Models

Introduction

Survival Models

Frailty Models

Survival and Frailty Models with Missing Covariates
Frailty Models with Measurement Errors
Joint Modeling Longitudinal and Survival Data

Introduction

Joint Modeling for Longitudinal Data and Survival Data

Two-Step Methods

Joint Likelihood Inference

Joint Models with Incomplete Data

Joint Modeling of Several Longitudinal Processes
Robust Mixed Effects Models

Introduction

Robust Methods

Mixed Effects Models with Robust Distributions
M-Estimators for Mixed Effects Models

Robust Inference for Mixed Effects Models with Incomplete Data
Generalized Estimating Equations (GEEs)

Introduction

Marginal Models

Estimating Equations with Incomplete Data

Discussion
Bayesian Mixed Effects Models

Introduction

Bayesian Methods

Bayesian Mixed Effects Models

Bayesian Mixed Models with Missing Data

Bayesian Models with Covariate Measurement Errors

Bayesian Joint Models of Longitudinal and Survival Data
Appendix: Background Materials

Likelihood Methods

The Gibbs Sampler and MCMC Methods

Rejection Sampling and Importance Sampling Methods

Numerical Integration and the Gauss–Hermite Quadrature Method

Optimization Methods and the Newton–Raphson Algorithm

Bootstrap Methods

Matrix Algebra and Vector Differential Calculus

References

Index

Abstract

Über Autor(innen)

Lang Wu is an associate professor in the Department of Statistics at the University of British Columbia in Vancouver, Canada.