Buch, Englisch, 336 Seiten, Format (B × H): 152 mm x 242 mm, Gewicht: 562 g
Buch, Englisch, 336 Seiten, Format (B × H): 152 mm x 242 mm, Gewicht: 562 g
ISBN: 978-1-58488-185-8
Verlag: CRC Press
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.
Zielgruppe
Professional Practice & Development
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Issues in Modelling Clustered Data and Motivating Examples. Model Families and Estimating Methods. Pseudo-Likelihood Estimation. Pseudo-Likelihood Inference. Flexible Polynomial Models. Assessing the Fit of a Model. Quantitative Risk Assessment. Model Misspecification. Exact Dose-Response Inference. Individual Level Covariates. Combined Continuous and Discrete Outcomes. Multilevel Modeling of Complex Survey Data.
