Applied Longitudinal Analysis

Front Cover
John Wiley & Sons, Jul 1, 2004 - Mathematics - 506 pages
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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 SASŪ
  • 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.


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1ntroduction to Longitudinal and Clustered Data
Basic Concepts
Linear Models for Longitudinal Continuous Data
Estimation and Statistical Inference
Analyzing Response Profiles
Parametric Curves
Modelling the Covariance
Strengths and Weaknesses of Covariance
Generalized Linear Mixed Effects Models
Contrasting Marginal and Mixed Effects Models
Advanced Topics for Longitudinal and Clustered Data
I5 Some Aspects of the Design of Longitudinal Studies
I6 Repeated Measures and Related Designs
I7 Multilevel Models
Appendix A Gentle Introduction to Vectors and Matrices
Appendix B Properties of Expectations and Variances

Residual Analyses and Diagnostics
I0 Review of Generalized Linear Models
Generalized Estimating

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About the author (2004)

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.