Applied Longitudinal Analysis

Name: Applied Longitudinal Analysis
Price: 23.25 USD
Availability: InStock
ISBN: 9780471214878

ISBN-10: 0471214876

ISBN-13: 9780471214878

Edition: 2004

Authors: Garrett M. Fitzmaurice, Nan Laird, James Ware, Garrett Fitzmaurice, Nan M. Laird

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'Applied Longitudinal Analysis' examines new methods for analyzing longitudinal data & the numerous applications to which these methodologies can be put. Models include general linear models, mixed effects models, & extensions of GLMs.

Book details

List price: $150.00
Copyright year: 2004
Publisher: John Wiley & Sons, Incorporated
Publication date: 7/1/2004
Binding: Hardcover
Pages: 536
Size: 6.25" wide x 9.25" long x 1.25" tall
Weight: 1.936
Language: English



Preface


Acknowledgments


Introduction to Longitudinal and Clustered Data


Longitudinal and Clustered Data


Introduction


Longitudinal and Clustered Data


Examples


Regression Models for Correlated Responses


Organization of This Book


Further Reading


Longitudinal Data: Basic Concepts


Introduction


Objectives of Longitudinal Analysis


Defining Features of Longitudinal Data


Example: Treatment of Lead-Exposed Children Trial


Sources of Correlation in Longitudinal Data


Further Reading


Problems


Linear Models for Longitudinal Continuous Data


Overview of Linear Models for Longitudinal Data


Introduction


Notation and Distributional Assumptions


Simple Descriptive Methods of Analysis


Modelling the Mean


Modelling the Covariance


Historical Approaches


Further Reading


Estimation and Statistical Inference


Introduction


Estimation: Maximum Likelihood


Missing Data Issues


Statistical Inference


Restricted Maximum Likelihood (REML) Estimation


Further Reading


Modelling the Mean: Analyzing Response Profiles


Introduction


Hypotheses Concerning Response Profiles


General Linear Model Formulation


Case Study


One-Degree-of-Freedom Tests for Group by Time Interaction


Adjustment for Baseline Response


Alternative Methods of Adjusting for Baseline Response


Strengths and Weaknesses of Analyzing Response Profiles


Computing: Analyzing Response Profiles Using PROC MIXED in SAS


Further Reading


Problems


Modelling the Mean: Parametric Curves


Introduction


Polynomial Trends in Time


Linear Splines


General Linear Model Formulation


Case Studies


Computing: Fitting Parametric Curves Using PROC MIXED in SAS


Further Reading


Problems


Modelling the Covariance


Introduction


Implications of Correlation among Longitudinal Data


Unstructured Covariance


Covariance Pattern Models


Choice among Covariance Pattern Models


Case Study


Discussion: Strengths and Weaknesses of Covariance Pattern Models


Computing: Fitting Covariance Pattern Models Using PROC MIXED in SAS


Further Reading


Problems


Linear Mixed Effects Models


Introduction


Linear Mixed Effects Models


Random Effects Covariance Structure


Two-Stage Random Effects Formulation


Choice among Random Effects Covariance Models


Prediction of Random Effects


Prediction and Shrinkage


Case Studies


Computing: Fitting Linear Mixed Effects Models Using PROC MIXED in SAS


Further Reading


Problems


Residual Analyses and Diagnostics


Introduction


Residuals


Transformed Residuals


Semi-Variogram


Case Study


Summary


Further Reading


Problems


Generalized Linear Models for Longitudinal Data


Review of Generalized Linear Models


Introduction


Salient Features of Generalized Linear Models


Illustrative Examples


Computing: Fitting Generalized Linear Models Using PROC GENMOD in SAS


Overview of Generalized Linear Models


Further Reading


Problems


Marginal Models: Generalized Estimating Equations (GEE)


Introduction


Marginal Models for Longitudinal Data


Estimation for Marginal Models: Generalized Estimating Equations


Case Studies


Computing: Generalized Estimating Equations Using PROC GENMOD in SAS


Distributional Assumptions for Marginal Models


Further Reading


Problems


Generalized Linear Mixed Effects Models


Introduction


Incorporating Random Effects in Generalized Linear Models


Interpretation of Regression Parameters


Estimation and Inference


Case Studies


Computing: Fitting Generalized Linear Mixed Models Using PROC NLNIXED in SAS


Further Reading


Problems


Contrasting Marginal and Mixed Effects Models


Introduction


Linear Models: A Special Case


Generalized Linear Models


Simple Numerical Illustration


Case Study


Conclusion


Further Reading


Advanced Topics for Longitudinal and Clustered Data


Missing Data and Dropout


Introduction


Hierarchy of Missing Data Mechanisms


Implications for Longitudinal Analysis


Dropout


Common Approaches for Handling Dropout


Case Study


Further Reading


Some Aspects of the Design of Longitudinal Studies


Introduction


Sample Size and Power


Interpretation of Stochastic Time-Varying Covariates


Longitudinal and Cross-Sectional Information


Further Reading


Repeated Measures and Related Designs


Introduction


Repeated Measures Designs


Multiple Source Data


Case Study 1: Repeated Measures Experiment


Case Study 2: Multiple Source Data


Summary


Further Reading


Multilevel Models


Introduction


Multilevel Data


Multilevel Linear Models


Multilevel Generalized Linear Models


Summary


Further Reading


Gentle Introduction to Vectors and Matrices


Properties of Expectations and Variances


Critical Points for a 50:50 Mixture of Chi-Squared Distributions


References


Index