Read online Wiley Series in Probability and Statistics: Applied Longitudinal Analysis 407 by James Ware in DJV, FB2
9780471214878 0471214876 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., 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 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. 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.
9780471214878 0471214876 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., 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 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. 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.