Longitudinal Data Analysis

ISBN-10: 0471420271

ISBN-13: 9780471420279

Edition: 2006

List price: $157.00
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Description:

This text presents and describes methods for analysis of longitudinal data, with a strong emphasis on application of these methods to problems in the biomedical and behavioral sciences. Applied Longitudinal Data Analysis is geared more toward users, and not developers, of statistics. Specific statistical procedures that the book will describe include: repeated measures analysis of variance, multivariate analysis of variance for repeated measures, random-effects regression models (RRM), covariance-structure models, and generalized-estimating equations (GEE) models.
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Book details

List price: $157.00
Copyright year: 2006
Publisher: John Wiley & Sons, Incorporated
Publication date: 4/7/2006
Binding: Hardcover
Pages: 360
Size: 6.25" wide x 9.50" long x 1.00" tall
Weight: 1.364
Language: English

DONALD HEDEKER, PHD, is Professor of Biostatistics in the Division of Epidemiology and Biostatistics, School of Public Health at the University of Illinois at Chicago. He is a Fellow of the American Statistical Association and the author of numerous peer-reviewed papers.ROBERT D. GIBBONS, PHD, is Director of the Center for Health Statistics; Professor of Biostatistics in the Division of Epidemiology and Biostatistics, School of Public Health; and Professor of Psychiatry in the College of Medicine, all at the University of Illinois at Chicago. He is a Fellow of the American Statistical Association and the author of numerous peer-reviewed papers.

Preface
Acknowledgments
Acronyms
Introduction
Advantages of Longitudinal Studies
Challenges of Longitudinal Data Analysis
Some General Notation
Data Layout
Analysis Considerations
General Approaches
The Simplest Longitudinal Analysis
Change Score Analysis
Analysis of Covariance of Post-test Scores
ANCOVA of Change Scores
Example
Summary
ANOVA Approaches to Longitudinal Data
Single-Sample Repeated Measures ANOVA
Design
Decomposing the Time Effect
Trend Analysis-Orthogonal Polynomial Contrasts
Change Relative to Baseline-Reference Cell Contrasts
Consecutive Time Comparisons-Profile Contrasts
Contrasting Each Timepoint to the Mean of Subsequent Timepoints-Helmert Contrasts
Contrasting Each Timepoint to the Mean of Others-Deviation Contrasts
Multiple Comparisons
Multiple-Sample Repeated Measures ANOVA
Testing for Group by Time Interaction
Testing for Subject Effect
Contrasts for Time Effects
Orthogonal Polynomial Partition of SS
Compound Symmetry and Sphericity
Sphericity
Illustration
Summary
MANOVA Approaches to Longitudinal Data
Data Layout for ANOVA versus MANOVA
MANOVA for Repeated Measurements
Growth Curve Analysis-Polynomial Representation
Extracting Univariate Repeated Measures ANOVA Results
Multivariate Test of the Time Effect
Tests of Specific Time Elements
MANOVA of Repeated Measures- s Sample Case
Extracting Univariate Repeated Measures ANOVA Results
Multivariate Tests
Illustration
Summary
Mixed-Effects Regression Models for Continuous Outcomes
Introduction
A Simple Linear Regression Model
Random Intercept MRM
Incomplete Data Across Time
Compound Symmetry and Intraclass Correlation
Inference
Psychiatric Dataset
Random Intercept Model Example
Random Intercept and Trend MRM
Random Intercept and Trend Example
Coding of Time
Example
Effect of Diagnosis on Time Trends
Matrix Formulation
Fit of Variance-Covariance Matrix
Model with Time-Varying Covariates
Within and Between-Subjects Effects for Time-Varying Covariates
Time Interactions with Time-Varying Covariates
Estimation
ML Bias in Estimation of Variance Parameters
Summary
Mixed-Effects Polynomial Regression Models
Introduction
Curvilinear Trend Model
Curvilinear Trend Example
Orthogonal Polynomials
Model Representations
Orthogonal Polynomial Trend Example
Translating Parameters
Higher-Order Polynomial Models
Cubic Trend Example
Summary
Covariance Pattern Models
Introduction
Covariance Pattern Models
Compound Symmetry Structure
First-Order Autoregressive Structure
Toeplitz or Banded Structure
Unstructured Form
Random-Effects Structure
Model Selection
Example
Summary
Mixed Regression Models with Autocorrelated Errors
Introduction
MRMs with AC Errors
AR(1) Errors
MA(1) Errors
ARMA(1,1) Errors
Toeplitz Errors
Nonstationary AR(1) Errors
Model Selection
Example
Summary
Generalized Estimating Equations (GEE) Models
Introduction
Generalized Linear Models (GLMs)
Generalized Estimating Equations (GEE) models
Working Correlation Forms
GEE Estimation
Example
Generalized Wald Tests for Model Comparison
Model Fit of Observed Proportions
Summary
Mixed-Effects Regression Models for Binary Outcomes
Introduction
Logistic Regression Model
Probit Regression Models
Threshold Concept
Mixed-Effects Logistic Regression Model
Intraclass Correlation
More General Mixed-Effects Models
Heterogeneous Variance Terms
Multilevel Representation
Response Functions
Estimation
Estimation of Random Effects and Probabilities
Multiple Random Effects
Integration over the Random-Effects Distribution
Illustration
Fixed-Effects Logistic Regression Model
Random Intercept Logistic Regression Model
Random Intercept and Trend Logistic Regression Model
Summary
Mixed-Effects Regression Models for Ordinal Outcomes
Introduction
Mixed-Effects Proportional Odds Model
Partial Proportional Odds
Models with Scaling Terms
Intraclass Correlation and Partitioning of Between- and Within-Cluster Variance
Survival Analysis Models
Intraclass Correlation
Estimation
Psychiatric Example
Health Services Research Example
Summary
Mixed-Effects Regression Models for Nominal Data
Mixed-Effects Multinomial Regression Model
Intraclass Correlation
Parameter Estimation
Health Services Research Example
Competing Risk Survival Models
Waiting for Organ Transplantation
Summary
Mixed-effects Regression Models for Counts
Poisson Regression Model
Modified Poisson Models
The ZIP Model
Mixed-Effects Models for Counts
Mixed-Effects Poisson Regression Model
Estimation of Random Effects
Mixed-Effects ZIP Regression Model
Illustration
Summary
Mixed-Effects Regression Models for Three-Level Data
Three-Level Mixed-Effects Linear Regression Model
Illustration
Three-Level Mixed-Effects Nonlinear Regression Models
Three-Level Mixed-Effects Probit Regression
Three-Level Logistic Regression Model for Dichotomous Outcomes
Illustration
More General Outcomes
Ordinal Outcomes
Nominal Outcomes
Count Outcomes
Summary
Missing Data in Longitudinal Studies
Introduction
Missing Data Mechanisms
Missing Completely at Random (MCAR)
Missing at Random (MAR)
Missing Not at Random (MNAR)
Models and Missing Data Mechanisms
MCAR Simulations
MAR and MNAR Simulations
Testing MCAR
Example
Models for Nonignorable Missingness
Selection Models
Mixed-Effects Selection Models
Example
Pattern-Mixture Models
Example
Summary
Bibliography
Topic Index
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