Topics in Modelling of Clustered Data

Topics in Modelling of Clustered Data

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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.
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Product details

  • Hardback | 336 pages
  • 151.9 x 241.8 x 22.6mm | 562.46g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 44 black & white illustrations, 52 black & white tables, 2 black & white halftones
  • 1584881852
  • 9781584881858

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
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