Hedeker / Gibbons Longitudinal Data Analysis

1. Auflage 2006
ISBN: 978-0-470-03647-1
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 368 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

Longitudinal data analysis for biomedical and behavioralsciences
This innovative book sets forth and describes methods for theanalysis of longitudinaldata, emphasizing applications to problemsin the biomedical and behavioral sciences. Reflecting the growingimportance and use of longitudinal data across many areas ofresearch, the text is designed to help users of statistics betteranalyze and understand this type of data.
Much of the material from the book grew out of a course taught byDr. Hedeker on longitudinal data analysis. The material is,therefore, thoroughly classroom tested and includes a number offeatures designed to help readers better understand and apply thematerial. Statistical procedures featured within the textinclude:
* Repeated measures analysis of variance
* Multivariate analysis of variance for repeated measures
* Random-effects regression models (RRM)
* Covariance-pattern models
* Generalized-estimating equations (GEE) models
* Generalizations of RRM and GEE for categorical outcomes
Practical in their approach, the authors emphasize the applicationsof the methods, using real-world examples for illustration. Somesyntax examples are provided, although the authors do not generallyfocus on software in this book. Several datasets and computersyntax examples are posted on this title's companion Web site. Theauthors intend to keep the syntax examples current as new versionsof the software programs emerge.
This text is designed for both undergraduate and graduate coursesin longitudinal data analysis. Instructors can take advantage ofoverheads and additional course materials available online foradopters. Applied statisticians in biomedicine and the socialsciences can also use the book as a convenient reference.

Autoren/Hrsg.

Hedeker, Donald

Gibbons, Robert D.

Inhaltsverzeichnis

Preface.
Acknowledgments.
Acronyms.
1. Introduction.
1.1 Advantages of Longitudinal Studies.
1.2 Challenges of Longitudinal Data Analysis.
1.3 Some General Notation.
1.4 Data Layout.
1.5 Analysis Considerations.
1.6 General Approaches.
1.7 The Simplest Longitudinal Analysis.
1.8 Summary.
2. ANOVA Approaches to Longitudinal Data.
2.1Single-Sample Repeated Measures ANOVA.
2.2 Multiple-Sample Repeated Measures ANOVA.
2.3 Illustration.
2.4 Summary.
3. MANOVA Approaches to Longitudinal Data.
3.1 Data Layout for ANOVA versus MANOVA.
3.2 MANOVA for Repeated Measurements.
3.3 MANOVA of Repeated Measures-s Sample Case.
3.4 Illustration.
3.5 Summary.
4. Mixed-Effects Regression Models for ContinuousOutcomes.
4.1 Introduction.
4.2 A Simple Linear Regression Model.
4.3 Random Intercept MRM.
4.4 Random Intercept and Trend MRM.
4.5 Matrix Formulation.
4.6 Estimation .
4.7 Summary.
5. Mixed-Effects Polynomial Regression Models.
5.1 Introduction.
5.2 Curvilinear Trend Model.
5.3 Orthogonal Polynomials.
5.4 Summary.
6. Covariance Pattern Models.
6.1 Introduction.
6.2 Covariance Pattern Models.
6.3 Model Selection.
6.4 Example.
6.5 Summary.
7. Mixed Regression Models with AutocorrelatedErrors.
7.1 Introduction.
7.2 MRMs with AC Errors.
7.3 Model Selection.
7.4 Example.
7.5 Summary.
8. Generalized Estimating Equations (GEE)Models.
8.1 Introduction.
8.2 Generalized Linear Models (GLMs).
8.3 Generalized Estimating Equations (GEE) Models.
8.4 GEE Estimation.
8.5 Example.
8.6 Summary.
9. Mixed-Effects Regression Models for BinaryOutcomes.
9.1 Introduction.
9.2 Logistic Regression Model.
9.3 Probit Regression Models.
9.4 Threshold Concept.
9.5 Mixed-Effects Logistic Regression Model.
9.6 Estimation.
9.7 Illustration.
9.8 Summary.
10. Mixed-Effects Regression Models for OrdinalOutcomes.
10.1 Introduction.
10.2 Mixed-Effects Proportional Odds Model.
10.3 Psychiatric Example.
10.4 Health Services Research Example.
10.5 Summary.
11. Mixed-Effects Regression Models for NominalData.
11.1 Mixed-Effects Multinomial Regression Model.
11.2 Health Services Research Example.
1 1.3 Competing Risk Survival Models.
11.4 Summary.
12. Mixed-effects Regression Models forCounts.
12.1 Poisson Regression Model.
12.2 Modified Poisson Models.
12.3 The ZIP Model.
12.4 Mixed-Effects Models for Counts.
12.5 Illustration.
12.6 Summary.
13. Mixed-Effects Regression Models for Three-LevelData.
13.1 Three-Level Mixed-Effects Linear Regression Model.
13.1.1 Illustration.
13.2 Three-Level Mixed-Effects Nonlinear Regression Models.
13.3 Summary.
14. Missing Data in Longitudinal Studies.
14.1 Introduction.
14.2 Missing Data Mechanisms.
14.3 Models and Missing Data Mechanisms.
14.4 Testing MCAR.
14.5 Models for Nonignorable Missingness.
14.6 Summary.
Bibliography.
Topic Index.

Über Autor(innen)

DONALD HEDEKER, PHD, is Professor of Biostatistics in theDivision of Epidemiology and Biostatistics, School of Public Healthat the University of Illinois at Chicago. He is a Fellow of theAmerican Statistical Association and the author of numerouspeer-reviewed papers.
ROBERT D. GIBBONS, PHD, is Director of the Center forHealth Statistics; Professor of Biostatistics in the Division ofEpidemiology and Biostatistics, School of Public Health; andProfessor of Psychiatry in the College of Medicine, all at theUniversity of Illinois at Chicago. He is a Fellow of the AmericanStatistical Association and the author of numerous peer-reviewedpapers.