Topics in Modelling of Clustered Data

<p>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. <br><br>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.<br><br>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.</p>