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
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