Longitudinal Data Analysis

Name: Longitudinal Data Analysis
Availability: InStock
ISBN: 9780471420279

ISBN-10: 0471420271

ISBN-13: 9780471420279

Edition: 2006

Authors: Donald Hedeker, Robert D. Gibbons

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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.

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: 1.694
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