Models for discrete longitudinal data /

Models for discrete longitudinal data /

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Bibliographic Details
Author / Creator:Molenberghs, Geert.
Imprint:New York ; London : Springer, 2005.
Description:xxii, 683 p. : ill.
Language:English
Series:Springer series in statistics
Subject:
Longitudinal method.
Multivariate analysis.
Longitudinal method.
Multivariate analysis.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/5727308
Hidden Bibliographic Details
Other authors / contributors:Verbeke, Geert.
ISBN:0387251448 (hbk.)
Notes:Includes bibliographical references and index.
Standard no.:9780387251448
Table of Contents:
  • Preface
  • Acknowledgments
  • I. Introductory Material
  • 1. Introduction
  • 2. Motivating Studies
  • 2.1. Introduction
  • 2.2. The Analgesic Trial
  • 2.3. The Toenail Data
  • 2.4. The Fluvoxamine Trial
  • 2.5. The Epilepsy Data
  • 2.6. The Project on Preterm and Small for Gestational Age Infants (POPS) Study
  • 2.7. National Toxicology Program Data
  • 2.8. The Sports Injuries Trial
  • 2.9. Age Related Macular Degeneration Trial
  • 3. Generalized Linear Models
  • 3.1. Introduction
  • 3.2. The Exponential Family
  • 3.3. The Generalized Linear Model (GLM)
  • 3.4. Examples
  • 3.5. Maximum Likelihood Estimation and Inference
  • 3.6. Logistic Regression for the Toenail Data
  • 3.7. Poisson Regression for the Epilepsy Data
  • 4. Linear Mixed Models for Gaussian Longitudinal Data
  • 4.1. Introduction
  • 4.2. Marginal Multivariate Model
  • 4.3. The Linear Mixed Model
  • 4.4. Estimation and Inference for the Marginal Model
  • 4.5. Inference for the Random Effects
  • 5. Model Families
  • 5.1. Introduction
  • 5.2. The Gaussian Case
  • 5.3. Model Families in General
  • 5.4. Inferential Paradigms
  • II. Marginal Models
  • 6. The Strength of Marginal Models
  • 6.1. Introduction
  • 6.2. Marginal Models in Contingency Tables
  • 6.3. British Occupational Status Study
  • 6.4. The Caithness Data
  • 6.5. Analysis of the Fluvoxamine Trial
  • 6.6. Extensions
  • 6.7. Relation to Latent Continuous Densities
  • 6.8. Conclusions and Perspective
  • 7. Likelihood-based Marginal Models
  • 7.1. Notation
  • 7.2. The Bahadur Model
  • 7.3. A General Framework for Fully Specified Marginal Models
  • 7.4. Maximum Likelihood Estimation
  • 7.5. An Influenza Study
  • 7.6. The Multivariate Probit Model
  • 7.7. The Dale Model
  • 7.8. Hybrid Marginal-conditional Specification
  • 7.9. A Cross-over Trial: An Example in Primary Dysmenorrhoea
  • 7.10. Multivariate Analysis of the POPS Data
  • 7.11. Longitudinal Analysis of the Fluvoxamine Study
  • 7.12. Appendix: Maximum Likelihood Estimation
  • 7.13. Appendix: The Multivariate Plackett Distribution
  • 7.14. Appendix: Maximum Likelihood Estimation for the Dale Model
  • 8. Generalized Estimating Equations
  • 8.1. Introduction
  • 8.2. Standard GEE Theory
  • 8.3. Alternative GEE Methods
  • 8.4. Prentice's GEE Method
  • 8.5. Second-order Generalized Estimating Equations (GEE2)
  • 8.6. GEE with Odds Ratios and Alternating Logistic Regression
  • 8.7. GEE2 Based on a Hybrid Marginal-conditional Model
  • 8.8. A Method Based on Linearization
  • 8.9. Analysis of the NTP Data
  • 8.10. The Heatshock Study
  • 8.11. The Sports Injuries Trial
  • 9. Pseudo-Likelihood
  • 9.1. Introduction
  • 9.2. Pseudo-Likelihood: Definition and Asymptotic Properties
  • 9.3. Pseudo-Likelihood Inference
  • 9.4. Marginal Pseudo-Likelihood
  • 9.5. Comparison with Generalized Estimating Equations
  • 9.6. Analysis of NTP Data
  • 10. Fitting Marginal Models with SAS
  • 10.1. Introduction
  • 10.2. The Toenail Data
  • 10.3. GEE1 with Correlations
  • 10.4. Alternating Logistic Regressions
  • 10.5. A Method Based on Linearization
  • 10.6. Programs for the NTP Data
  • 10.7. Alternative Software Tools
  • III. Conditional Models
  • 11. Conditional Models
  • 11.1. Introduction
  • 11.2. Conditional Models
  • 11.3. Marginal versus Conditional Models
  • 11.4. Analysis of the NTP Data
  • 11.5. Transition Models
  • 12. Pseudo-Likehood
  • 12.1. Introduction
  • 12.2. Pseudo-Likelihood for a Single Repeated Binary Outcome
  • 12.3. Pseudo-Likelihood for a Multivariate Repeated Binary Outcome
  • 12.4. Analysis of the NTP Data
  • IV. Subject-specific Models
  • 13. From Subject-specific to Random-effects Models
  • 13.1. Introduction
  • 13.2. General Model Formulation
  • 13.3. Three Ways to Handle Subject-specific Parameters
  • 13.4. Random-effects Models: Special Cases
  • 14. The Generalized Linear Mixed Model (GLMM)
  • 14.1. Introduction
  • 14.2. Model Formulation and Approaches to Estimation
  • 14.3. Estimation: Approximation of the Integrand
  • 14.4. Estimation: Approximation of the Data
  • 14.5. Estimation: Approximation of the Integral
  • 14.6. Inference in Generalized Linear Mixed Models
  • 14.7. Analyzing the NTP Data
  • 14.8. Analyzing the Toenail Data
  • 15. Fitting Generalized Linear Mixed Models with SAS
  • 15.1. Introduction
  • 15.2. The GLIMMIX Procedure for Quasi-Likelihood
  • 15.3. The GLIMMIX Macro for Quasi-Likelihood
  • 15.4. The NLMIXED Procedure for Numerical Quadrature
  • 15.5. Alternative Software Tools
  • 16. Marginal versus Random-effects Models
  • 16.1. Introduction
  • 16.2. Example: The Toenail Data
  • 16.3. Parameter Interpretation
  • 16.4. Toenail Data: Marginal versus Mixed Models
  • 16.5. Analysis of the NTP Data
  • V. Case Studies and Extensions
  • 17. The Analgesic Trial
  • 17.1. Introduction
  • 17.2. Marginal Analyses of the Analgesic Trial
  • 17.3. Random-effects Analyses of the Analgesic Trial
  • 17.4. Comparing Marginal and Random-effects Analyses
  • 17.5. Programs for the Analgesic Trial
  • 18. Ordinal Data
  • 18.1. Regression Models for Ordinal Data
  • 18.2. Marginal Models for Repeated Ordinal Data
  • 18.3. Random-effects Models for Repeated Ordinal Data
  • 18.4. Ordinal Analysis of the Analgesic Trial
  • 18.5. Programs for the Analgesic Trial
  • 19. The Epilepsy Data
  • 19.1. Introduction
  • 19.2. A Marginal GEE Analysis
  • 19.3. A Generalized Linear Mixed Model
  • 19.4. Marginalizing the Mixed Model
  • 20. Non-linear Models
  • 20.1. Introduction
  • 20.2. Univariate Non-linear Models
  • 20.3. The Indomethacin Study: Non-hierarchical Analysis
  • 20.4. Non-linear Models for Longitudinal Data
  • 20.5. Non-linear Mixed Models
  • 20.6. The Orange Tree Data
  • 20.7. Pharmacokinetic and Pharmacodynamic Models
  • 20.8. The Songbird Data
  • 20.9. Discrete Outcomes
  • 20.10. Hypothesis Testing and Non-linear Models
  • 20.11. Flexible Functions
  • 20.12. Using SAS for Non-linear Mixed-effects Models
  • 21. Pseudo-Likelihood for a Hierarchical Model
  • 21.1. Introduction
  • 21.2. Pseudo-Likelihood Estimation
  • 21.3. Two Binary Endpoints
  • 21.4. A Meta-analysis of Trials in Schizophrenic Subjects
  • 21.5. Concluding Remarks
  • 22. Random-effects Models with Serial Correlation
  • 22.1. Introduction
  • 22.2. A Multilevel Probit Model with Autocorrelation
  • 22.3. Parameter Estimation for the Multilevel Probit Model
  • 22.4. A Generalized Linear Mixed Model with Autocorrelation
  • 22.5. A Meta-analysis of Trials in Schizophrenic Subjects
  • 22.6. SAS Code for Random-effects Models with Autocorrelation
  • 22.7. Concluding Remarks
  • 23. Non-Gaussian Random Effects
  • 23.1. Introduction
  • 23.2. The Heterogeneity Model
  • 23.3. Estimation and Inference
  • 23.4. Empirical Bayes Estimation and Classification
  • 23.5. The Verbal Aggression Data
  • 23.6. Concluding Remarks
  • 24. Joint Continuous and Discrete Responses
  • 24.1. Introduction
  • 24.2. A Continuous and a Binary Endpoint
  • 24.3. Hierarchical Joint Models
  • 24.4. Age Related Macular Degeneration Trial
  • 24.5. Joint Models in SAS
  • 24.6. Concluding Remarks
  • 25. High-dimensional Joint Models
  • 25.1. Introduction
  • 25.2. Joint Mixed Model
  • 25.3. Model Fitting and Inference
  • 25.4. A Study in Psycho-Cognitive Functioning
  • VI. Missing Data
  • 26. Missing Data Concepts
  • 26.1. Introduction
  • 26.2. A Formal Taxonomy
  • 27. Simple Methods, Direct Likelihood, and WGEE
  • 27.1. Introduction
  • 27.2. Longitudinal Analysis or Not?
  • 27.3. Simple Methods
  • 27.4. Bias in LOCF, CC, and Ignorable Likelihood
  • 27.5. Weighted Generalized Estimating Equations
  • 27.6. The Depression Trial
  • 27.7. Age Related Macular Degeneration Trial
  • 27.8. The Analgesic Trial
  • 28. Multiple Imputation and the EM Algorithm
  • 28.1. Introduction
  • 28.2. Multiple Imputation
  • 28.3. The Expectation-Maximization Algorithm
  • 28.4. Which Method to Use?
  • 28.5. Age Related Macular Degeneration Study
  • 28.6. Concluding Remarks
  • 29. Selection Models
  • 29.1. Introduction
  • 29.2. An MNAR Dale Model
  • 29.3. A Model for Non-monotone Missingness
  • 29.4. Concluding Remarks
  • 30. Pattern-mixture Models
  • 30.1. Introduction
  • 30.2. Pattern-mixture Modeling Approach
  • 30.3. Identifying Restriction Strategies
  • 30.4. A Unifying Framework for Selection and Pattern-mixture Models
  • 30.5. Selection Models versus Pattern-mixture Models
  • 30.6. Analysis of the Fluvoxamine Data
  • 30.7. Concluding Remarks
  • 31. Sensitivity Analysis
  • 31.1. Introduction
  • 31.2. Sensitivity Analysis for Selection Models
  • 31.3. A Local Influence Approach for Ordinal Data with Dropout
  • 31.4. A Local Influence Approach for Incomplete Binary Data
  • 31.5. Interval of Ignorance
  • 31.6. Sensitivity Analysis and Pattern-mixture Models
  • 31.7. Concluding Remarks
  • 32. Incomplete Data and SAS
  • 32.1. Introduction
  • 32.2. Complete Case Analysis
  • 32.3. Last Observation Carried Forward
  • 32.4. Direct Likelihood
  • 32.5. Weighted Estimating Equations (WGEE)
  • 32.6. Multiple Imputation
  • 32.7. The EM Algorithm
  • 32.8. MNAR Models and Sensitivity Analysis Tools
  • References
  • Index
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