Applied Longitudinal Analysis
1st Edition
Published on 1. July 2004
Book
Hardback
536 pages
978-0-471-21487-8 (ISBN)
Description
A rigorous, systematic presentation of modern longitudinal analysis
Longitudinal studies, employing repeated measurement of subjects over time, play a prominent role in the health and medical sciences as well as in pharmaceutical studies. An important strategy in modern clinical research, they provide valuable insights into both the development and persistence of disease and those factors that can alter the course of disease development.
Written at a technical level suitable for researchers and graduate students, Applied Longitudinal Analysis provides a rigorous and comprehensive description of modern methods for analyzing longitudinal data. Focusing on General Linear and Mixed Effects Models for continuous responses, and extensions of Generalized Linear Models for discrete responses, the authors discuss in detail the relationships among these different models, including their underlying assumptions and relative merits. The book features:
* A focus on practical applications, utilizing a wide range of examples drawn from real-world studies
* Coverage of modern methods of regression analysis for correlated data
* Analyses utilizing SAS(r)
* Multiple exercises and "homework" problems for review
An accompanying Web site features twenty-five real data sets used throughout the text, in addition to programming statements and selected computer output for the examples.
Longitudinal studies, employing repeated measurement of subjects over time, play a prominent role in the health and medical sciences as well as in pharmaceutical studies. An important strategy in modern clinical research, they provide valuable insights into both the development and persistence of disease and those factors that can alter the course of disease development.
Written at a technical level suitable for researchers and graduate students, Applied Longitudinal Analysis provides a rigorous and comprehensive description of modern methods for analyzing longitudinal data. Focusing on General Linear and Mixed Effects Models for continuous responses, and extensions of Generalized Linear Models for discrete responses, the authors discuss in detail the relationships among these different models, including their underlying assumptions and relative merits. The book features:
* A focus on practical applications, utilizing a wide range of examples drawn from real-world studies
* Coverage of modern methods of regression analysis for correlated data
* Analyses utilizing SAS(r)
* Multiple exercises and "homework" problems for review
An accompanying Web site features twenty-five real data sets used throughout the text, in addition to programming statements and selected computer output for the examples.
Reviews / Votes
"Every statistician should have this well written book." (Journal of Statistical Computation and Simulation, March 2006) "This [book on longitudinal analysis]...is certainly my favorite. This is a large and comprehensive book with perfect balance between development of methods and application of methodology." (Technometrics, May 2005) "...should be on the shelf of everyone interested in acquiring a modeler's or practitioner's perspective on longitudinal data analysis." (Journal of the American Statistical Association, June 2005)More details
Series
Edition
1., Auflage
Language
English
Place of publication
New York
United States
Publishing group
John Wiley and Sons Ltd
Target group
Professional and scholarly
Illustrations
Illustrations
Dimensions
Height: 23.9 cm
Width: 16.2 cm
Thickness: 28 mm
Weight
879 gr
ISBN-13
978-0-471-21487-8 (9780471214878)
Schweitzer Classification
Other editions
New editions
Garrett M. Fitzmaurice | Nan M. Laird | James H. Ware
Applied Longitudinal Analysis
Book
09/2011
2nd Edition
Wiley
€137.50
Shipment within 10-20 days
Persons
GARRETT M. FITZMAURICE, ScD, is Associate Professor of Biostatistics at the Harvard School of Public Health.
NAN M. LAIRD, PhD, is Professor of Biostatistics at the Harvard School of Public Health.
JAMES H. WARE, PhD, is Frederick Mosteller Professor of Biostatistics and Dean for Academic Affairs at the Harvard School of Public Health.
All three authors are Fellows of the American Statistical Association and members of the International Statistical Institute.
NAN M. LAIRD, PhD, is Professor of Biostatistics at the Harvard School of Public Health.
JAMES H. WARE, PhD, is Frederick Mosteller Professor of Biostatistics and Dean for Academic Affairs at the Harvard School of Public Health.
All three authors are Fellows of the American Statistical Association and members of the International Statistical Institute.
Content
Preface.
Acknowledgments.
PART I: INTRODUCTION TO LONGITUDINAL AND CLUSTERED DATA.
1. Longitudinal and Clustered Data.
2. Longitudinal Data: Basic Concepts.
PART II: LINEAR MODELS FOR LONGITUDINAL CONTINUOUS DATA.
3. Overview of Linear Models for Longitudinal Data.
4. Estimation and Statistical Inference.
5. Modelling the Mean: Analyzing Response Profiles.
6. Modelling the Mean: Parametric Curves.
7. Modelling the Covariance.
8. Linear Mixed Effects Models.
9. Residual Analyses and Diagnostics.
PART III: GENERALIZED LINEAR MODELS FOR LONGITUDINAL DATA.
10. Review of Generalized Linear Models.
11. Marginal Models: Generalized Estimating Equations (GEE).
12. Generalized Linear Mixed Effects Models.
13. Contrasting Marginal and Mixed Effects Models.
PART IV: ADVANCED TOPICS FOR LONGITUDINAL AND CLUSTERED DATA.
14. Missing Data and Dropout.
15. Some Aspects of the Design of Longitudinal Studies.
16. Repeated Measures and Related Designs.
17. Multilevel Models.
Appendix A: Gentle Introduction to Vectors and Matrices.
Appendix B: Properties of Expectations and Variances.
Appendix C: Critical Points for a 50:50 Mixture of Chi-Squared Distributions.
References.
Index.
Acknowledgments.
PART I: INTRODUCTION TO LONGITUDINAL AND CLUSTERED DATA.
1. Longitudinal and Clustered Data.
2. Longitudinal Data: Basic Concepts.
PART II: LINEAR MODELS FOR LONGITUDINAL CONTINUOUS DATA.
3. Overview of Linear Models for Longitudinal Data.
4. Estimation and Statistical Inference.
5. Modelling the Mean: Analyzing Response Profiles.
6. Modelling the Mean: Parametric Curves.
7. Modelling the Covariance.
8. Linear Mixed Effects Models.
9. Residual Analyses and Diagnostics.
PART III: GENERALIZED LINEAR MODELS FOR LONGITUDINAL DATA.
10. Review of Generalized Linear Models.
11. Marginal Models: Generalized Estimating Equations (GEE).
12. Generalized Linear Mixed Effects Models.
13. Contrasting Marginal and Mixed Effects Models.
PART IV: ADVANCED TOPICS FOR LONGITUDINAL AND CLUSTERED DATA.
14. Missing Data and Dropout.
15. Some Aspects of the Design of Longitudinal Studies.
16. Repeated Measures and Related Designs.
17. Multilevel Models.
Appendix A: Gentle Introduction to Vectors and Matrices.
Appendix B: Properties of Expectations and Variances.
Appendix C: Critical Points for a 50:50 Mixture of Chi-Squared Distributions.
References.
Index.