Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS

Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS

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Released September 2012
Publisher(s): SAS Institute
ISBN: 9781599946474

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Book description

Edward F. Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models. Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately.

Table of contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Contents
  5. Preface
  6. Acknowledgments
  7. 1: Introduction
    1. 1.1: Correlated Response Data
    2. 1.2: Explanatory Variables
    3. 1.3: Types of Models
    4. 1.4: Some Examples
    5. 1.5: Summary Features
  8. I: Linear Models
    1. 2: Marginal Linear Models—Normal Theory
      1. 2.1: The Marginal Linear Model (LM)
      2. 2.2: Examples
      3. 2.3: Summary
    2. 3: Linear Mixed-Effects Models—Normal Theory
      1. 3.1: The Linear Mixed-Effects (LME) Model
      2. 3.2: Examples
      3. 3.3: Summary
  9. II: Nonlinear Models
    1. 4: Generalized Linear and Nonlinear Models
      1. 4.1: The Generalized Linear Model (GLIM)
      2. 4.2: The GLIM for Correlated Response Data
      3. 4.3: Examples of GLIM's
      4. 4.4: The Generalized Nonlinear Model (GNLM)
      5. 4.5: Examples of GNLM's
      6. 4.6: Computational Considerations
      7. 4.7: Summary
    2. 5: Generalized Linear and Nonlinear Mixed-Effects Models
      1. 5.1: The Generalized Linear Mixed-Effects (GLME) Model
      2. 5.2: Examples of GLME Models
      3. 5.3: The Generalized Nonlinear Mixed-Effects (GNLME) Model
      4. 5.4: Examples of GNLME Models
      5. 5.5: Summary
  10. III: Further Topics
    1. 6: Missing Data in Longitudinal Clinical Trials
      1. 6.1: Background
      2. 6.2: Missing Data Mechanisms
      3. 6.3: Dropout Mechanisms
      4. 6.4: Methods of Analysis Under MAR
      5. 6.5: Sensitivity Analysis Under MNAR
      6. 6.6: Missing Data—Case Studies
      7. 6.7: Summary
    2. 7: Additional Topics and Applications
      1. 7.1: Mixed Models with Non-Gaussian Random Effects
      2. 7.2: Pharmacokinetic Applications
      3. 7.3: Joint Modeling of Longitudinal Data and Survival Data
  11. IV: Appendices
    1. A: Some Useful Matrix Notation and Results
      1. A.1: Matrix Notation and Results
    2. B: Additional Results on Estimation
      1. B.1: The Different Estimators for Mixed-Effects Models
      2. B.2: Comparing Large Sample Properties of the Different Estimators
    3. C: Datasets
      1. C.1: Dental Growth Data
      2. C.2: Bone Mineral Density Data
      3. C.3: ADEMEX Adequacy Data
      4. C.4: MCM2 Biomarker Data
      5. C.5: Estrogen Hormone Data
      6. C.6: ADEMEX Peritonitis and Hospitalization Data
      7. C.7: Respiratory Disorder Data
      8. C.8: Epileptic Seizure Data
      9. C.9: Schizophrenia Data
      10. C.10: LDH Enzyme Leakage Data
      11. C.11: Orange Tree Data
      12. C.12: Soybean Growth Data
      13. C.13: High Flux Hemodialyzer Data
      14. C.14: Cefamandole Pharmacokinetic Data
      15. C.15: MDRD data
      16. C.16: Theophylline Data
      17. C.17: Phenobarbital Data
      18. C.18: ADEMEX GFR and Survival Data
    4. D: Select SAS Macros
      1. D.1: The GOF Macro
      2. D.2: The GLIMMIX_GOF Macro
      3. D.3: The CCC Macro
      4. D.4: The CONCORR Macro
      5. D.5: The COVPARMS Macro
      6. D.6: The VECH Macro
  12. Reference
  13. Index

Product information

  • Title: Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS
  • Author(s):
  • Release date: September 2012
  • Publisher(s): SAS Institute
  • ISBN: 9781599946474