Topics in Modelling of Clustered Data
Features
Summary
Many methods for analyzing clustered data exist, all with advantages and limitations in particular applications. Compiled from the contributions of leading specialists in the field, Topics in Modelling of Clustered Data describes the tools and techniques for modelling the clustered data often encountered in medical, biological, environmental, and social science studies. It focuses on providing a comprehensive treatment of marginal, conditional, and random effects models using, among others, likelihood, pseudo-likelihood, and generalized estimating equations methods.
The authors motivate and illustrate all aspects of these models in a variety of real applications. They discuss several variations and extensions, including individual-level covariates and combined continuous and discrete outcomes. Flexible modelling with fractional and local polynomials, omnibus lack-of-fit tests, robustification against misspecification, exact, and bootstrap inferential procedures all receive extensive treatment. The applications discussed center primarily, but not exclusively, on developmental toxicity, which leads naturally to discussion of other methodologies, including risk assessment and dose-response modelling.
Clearly written, Topics in Modelling of Clustered Data offers a practical, easily accessible survey of important modelling issues. Overview models give structure to a multitude of approaches, figures help readers visualize model characteristics, and a generous use of examples illustrates all aspects of the modelling process.
Table of Contents
INTRODUCTION
Correlated Data Settings
Developmental Toxicity Studies
Complex Surveys
Other Relevant Settings
Reading Guide
MOTIVATING EXAMPLES
National Toxicology Program Data
Heatshock Studies
Belgian Health Interview Survey
POPS Data
Low-Iron Rat Teratology Data
The Wisconsin Diabetes Study
Congenital Ophthalmic Defects
A Developmental Toxicology Study
ISSUES IN MODELING CLUSTERED DATA
Choosing a Model Family
Joint Continuous and Discrete Outcomes
Likelihood Misspecification and Alternative Methods
Risk Assessment
MODEL FAMILIES
Marginal Models
Conditional Models
Cluster-Specific Models
GENERALIZED ESTIMATING EQUATIONS
General Theory
Clustered Binary Data
PSEUDO-LIKELIHOOD ESTIMATION
Pseudo-Likelihood: Definition and Asymptotic Properties
Relative Efficiency of PL versus ML
Pseudo-Likelihood and Generalized Estimating Equations
PSEUDO-LIKELIHOOD INFERENCE
Test Statistics
Simulation Results
Illustration: EG Data
FLEXIBLE POLYNOMIAL MODELS
Fractional Polynomial Models
Local Polynomial Models
Other Flexible Polynomial Methods and Extensions
ASSESSING THE FIT OF A MODEL
A Hosmer-Lemeshow Approach for Likelihood Based Models
Order Selection Tests
Data-Driven Tests in Multiple Regression
Testing Goodness of Fit
QUANTITATIVE RISK ASSESSMENT
Expressing Risks
Analysis of NTP Data
Asymptotic Study
Concluding Remarks
MODEL MISSPECIFICATION
Implications of Misspecification on Dase Effect Assessment
A Robust Bootstrap Procedure
Implications of Misspecification on Safe Dose Determination
A Profile Score Approach
EXACT DOSE-RESPONSE INFERENCE
Exact Nonparametric Dose-Response Inference
Simulation Study
Concluding Remarks
INDIVIDUAL LEVEL COVARIATES
Cluster-Specific Models
Population-Averaged Models
Efficiency of Modeling Approaches
Analysis of Heatshock Data
Continuous Outcomes
Concluding Remarks
COMBINED CONTINUOUS AND DISCRETE OUTCOMES
Models for Bivariate Data of a Mixed Nature
Application to Quantitative Risk Assessment
Discussion
MULTILEVEL MODELING OF COMPLEX SURVEY DATA
Multilevel Models
Application to the HIS
Concluding Remarks
APPENDIX: BAHADUR PARAMETER SPACE
REFERENCES
INDEX
Editorial Reviews
" … the editors have done a commendable job at ensuring a common notation and ample cross-referencing between chapters … examples are easily found … A definite strength of the book is the comprehensive, well written and informative treatment of marginal approaches, in particular GEE (Chapter 5) and marginal models such as the Bahadur, George-Bowman and Dale model (Chapter 4 and Appendix)."
-Anders Skrondal and Sophia Rabe-Hesketh, Statistics in Medicine, Vol. 23, 2004
