Dose-Response Analysis Using R

NOTE EDITORE

Nowadays the term dose-response is used in many different contexts and many different scientific disciplines including agriculture, biochemistry, chemistry, environmental sciences, genetics, pharmacology, plant sciences, toxicology, and zoology.In the 1940 and 1950s, dose-response analysis was intimately linked to evaluation of toxicity in terms of binary responses, such as immobility and mortality, with a limited number of doses of a toxic compound being compared to a control group (dose 0). Later, dose-response analysis has been extended to other types of data and to more complex experimental designs. Moreover, estimation of model parameters has undergone a dramatic change, from struggling with cumbersome manual operations and transformations with pen and paper to rapid calculations on any laptop. Advances in statistical software have fueled this development.Key Features:Provides a practical and comprehensive overview of dose-response analysis. Includes numerous real data examples to illustrate the methodology. R code is integrated into the text to give guidance on applying the methods. Written with minimal mathematics to be suitable for practitioners. Includes code and datasets on the book’s GitHub: https://github.com/DoseResponse.This book focuses on estimation and interpretation of entirely parametric nonlinear dose-response models using the powerful statistical environment R. Specifically, this book introduces dose-response analysis of continuous, binomial, count, multinomial, and event-time dose-response data. The statistical models used are partly special cases, partly extensions of nonlinear regression models, generalized linear and nonlinear regression models, and nonlinear mixed-effects models (for hierarchical dose-response data). Both simple and complex dose-response experiments will be analyzed.

SOMMARIO

Continuous dataBinary and binomial dose-response dataCount dose-response dataMultinomial dose-response dataTime-to-event-response dataBenchmark dose estimationHierarchical nonlinear modelsAppendix A: EstimationAppendix B: Dose-response model functionsAppendix C: More R CodeBibliography, Index

AUTORE

Christian Ritz is an Associate Professor at the University of Copenhagen, Denmark.Signe M. Jensen is an Assistant Professor at the University of Copenhagen, Denmark.Daniel Gerhard is a Senior Lecturer at the University of Caterbury, New Zealand.Jens Carl Streibig is Professor Emeritus at the University of Copenhagen, Denmark.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9781032091815
• Dimensioni: 9.25 x 6.25 in Ø 0.75 lb
• Formato: Brossura
• Illustration Notes: 47 b/w images
• Pagine Arabe: 226