Dose-Response Analysis Using R

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 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: github/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.