226 Pages 47 B/W Illustrations
    by Chapman & Hall

    226 Pages 47 B/W Illustrations
    by Chapman & Hall

    226 Pages 47 B/W Illustrations
    by Chapman & Hall

    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.

    Continuous data

    Binary and binomial dose-response data

    Count dose-response data

    Multinomial dose-response data

    Time-to-event-response data

    Benchmark dose estimation

    Hierarchical nonlinear models

    Appendix A: Estimation

    Appendix B: Dose-response model functions

    Appendix C: More R Code

    Bibliography, Index


    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.

    "Dose-response modelling is an important topic in several disciplines ranging from chemistry, biology, (eco)toxicology to medicine. The statistical programming language R currently provides the most advanced dose response modelling framework, yet a comprehensive resource that gives guidance on implementation is missing. This book fills this void and with its accessible style and brevity is likely to become the standard authority resource for dose response modelling in R. The book is structured along dose response models for different types of responses (e.g. continuous, counts) and also contains chapters on more advanced topics such as hierarchical models. It takes an example-based approach, allowing the reader to reproduce every step of the analysis for selected case studies. Importantly, this empowers readers to apply the models to their own problems and helps with interpretation. Overall, the book is a valuable resource for both individuals new to dose response modelling and as a reference for individuals already familiar with the models and concepts, who want to fit dose response models in R. If you want a book that provides you with the R code and interpretation of simple and complex dose response models, this is the book for you!" 
    - Ralf Schafer, University Koblenz-Landau

    "Analysis of dose-response curves is one of the critical ways used to assess the effect of a toxicant (or other treatments) on plant growth. I have done many such analyses using the drc module in R developed by the authors of Dose-Response Analysis Using R. This new book is a wonderful new compendium of methodologies illustrated with relevant examples to conduct such analyses properly. A ‘Must Have’ resource for anyone relying on dose-response analysis in their work."
    Franck E. Dayan, Agricultural Biology Department, Colorado State University

    "This book will be useful to both applied statisticians and a wide range of biologists.  It combines a text on statistical modeling of dose-response data with lots of examples of using R and the drc package for data analysis.  Appendix B, describing models for dose-response data, is an extremely useful and thorough overview of the huge number of possible models.  It contains cross-references between equivalent models and a wealth of literature citations from diverse fields.  This alone is worth the price of the book. The examples are drawn from the published literature.  They provide a nice range of complexity, starting with simple introductions and finishing with quite complex analyses."
    - Philip Dixon, Iowa State University

    "Dose-Response Analysis Using R by Christian Ritz, Signe Marie Jensen, Daniel Gerhard, and Jens Carl Streibig has been published in The R Series of CRC press...The purpose of this book is to give a comprehensive overview and demonstrate the use of doseresponse analysis methods in R on real-life data sets. The focus is on model
    fitting and interpretation...the book is very friendly to non-R users, explaining code in a clear way and is therefore perfectly suitable for R novices...The self-sufficiency of the chapters is evident from the way the necessary R tools are listed at the beginning of the chapter. However, the early chapters provide more explanation about the details of the R codes and consist of simpler examples and therefore, especially for readers who are unfamiliar with R, reading the chapters in the order of appearance may be more advantageous. Having said this, the book can be used as an ‘R cookbook’ for dose-response analysis, consulting only the chapter or
    example of interest...This book can be of interest to (bio)statisticians and biologists with or without previous knowledge of R. It also can be used as a reference book."
    - Anikó Lovik, ISCB News, July2020