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. In the 1940 and 1950s, dose-response analysis was inti...
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| Language: | English |
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Milton
CRC Press
19.07.2019
CRC Press LLC Chapman & Hall |
| Edition: | 1 |
| Series: | Chapman & Hall/CRC The R Series |
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| ISBN: | 1138034312, 9781032091815, 9781138034310, 1032091819 |
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| Abstract | 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
"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
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. |
|---|---|
| AbstractList | 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
"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
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. 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. Dose-response analysis is a technique used to determine the safe and hazardous dosage of drugs, pollutants, and other substances to which humans and other organisms are exposed. This book provides a practical and comprehensive overview of methods for dose-response analysis. 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. |
| Author | Christian Ritz Signe Marie Jensen Jens Carl Streibig Daniel Gerhard |
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| Snippet | Nowadays the term dose-response is used in many different contexts and many different scientific disciplines including agriculture, biochemistry, chemistry,... Dose-response analysis is a technique used to determine the safe and hazardous dosage of drugs, pollutants, and other substances to which humans and other... |
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| SubjectTerms | BIOMEDICALSCIENCEnetBASE BIOSCIENCEnetBASE chemical continuous dose-response data drug Drugs Drugs-Dose-response relationship event-time dose-response data exposure-response nonlinear dose-response models nonlinear regression pollutant SCI-TECHnetBASE Statistical Computing Statistics for the Biological Sciences STATSnetBASE STMnetBASE toxicity evaluation Toxicology |
| TableOfContents | Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1. Continuous data -- 1.1 Analysis of single dose-response curves -- 1.1.1 Inhibitory effect of secalonic acid -- 1.1.1.1 Fitting the model -- 1.1.1.2 Estimation of arbitrary ED values -- 1.1.2 Data from a fish test in ecotoxicology -- 1.1.3 Ferulic acid as an herbicide -- 1.1.4 Glyphosate in barley -- 1.1.5 Lower limits for dose-response data -- 1.1.6 A hormesis effect on lettuce growth -- 1.1.7 Nonlinear calibration -- 1.2 Analysis of multiple dose-response curves -- 1.2.1 Effect of an herbicide mixture on Galium aparine -- 1.2.2 Glyphosate and bentazone treatment of Sinapis alba -- 1.2.2.1 A joint dose-response model -- 1.2.2.2 Fitting separate dose-response models -- 2. Binary and binomial dose-response data -- 2.1 Analysis of single dose-response curves -- 2.1.1 Acute inhalation toxicity test -- 2.1.1.1 Link to ordinary logistic regression -- 2.1.2 Tumor incidence -- 2.1.3 Earthworm toxicity test: Abbott's formula -- 2.1.4 Another earthworms toxicity test: Estimating the upper limit -- 2.2 Analysis of multiple dose-response curves -- 2.2.1 Toxicity of fluoranthene under different ultraviolet radiation -- 2.2.2 Toxicity of different types of selenium -- 3. Count dose-response data -- 3.1 Analysis of single dose-response curves -- 3.1.1 Counting number of fronds -- 3.1.2 Counting offspring: Modeling hormesis -- 3.1.3 More counting offspring: Varying observation periods -- 3.2 Analysis of multiple dose-response curves -- 3.2.1 Counting bacteria colonies: Wadley's problem -- 4. Multinomial dose-response data -- 4.1 Trichotomous data -- 4.1.1 Insecticide residues -- 4.1.2 Effect of two arboviruses on chicken embryos -- 5. Time-to-event-response data -- 5.1 Analysis of a single germination curve -- 5.1.1 Germination of Stellaria media seeds 5.2 Analysis of data from multiple germination curves -- 5.2.1 Time to death of daphnias -- 5.2.1.1 Step 1 -- 5.2.1.2 Step 2 -- 5.2.2 A hierarchical three-way factorial design -- 5.2.2.1 Step 1 -- 5.2.2.2 Step 2 -- 6. Benchmark dose estimation -- 6.1 Binomial dose-response data -- 6.1.1 Pathogens in food -- 6.1.2 Chromosomal damage -- 6.1.3 Tumor incidence continued: Integration of historical data -- 6.2 Continuous dose-response data -- 6.2.1 Toxicity of copper in an ecosystem with giant kelp -- 6.2.2 Toxicity of an antituberculosis drug -- 6.3 Model averaging -- 6.3.1 Pathogens in food revisited -- 6.3.2 Toxicity of an antituberculosis drug revisited -- 7. Hierarchical nonlinear models -- 7.1 Normally distributed dose-response data -- 7.2 The R package medrc -- 7.2.1 In vitro effects of the fungicide vinclozolin -- 7.2.2 Inhibition of photosynthesis in spinach -- 7.2.3 Herbicides with auxin effects -- 7.2.4 Drought stress resistance in Brassica oleracea -- Appendix A: Estimation -- A.1 Nonlinear least squares -- A.2 Maximum likelihood estimation -- A.2.1 Binomial dose-response data -- A.2.2 Count dose-response data -- A.2.2.1 The Poisson distribution -- A.2.2.2 The negative-binomial distribution -- A.2.3 Time-to-event-response data -- A.3 The transform-both-sides approach -- A.4 Robust estimation -- A.5 Sandwich variance estimators -- A.6 Constrained estimation -- A.7 Two-stage estimation for hierarchical models -- A.7.1 Technical replicates -- A.7.2 Two-stage approaches -- A.7.3 Lindstrom-Bates algorithm -- A.8 Starting values and self-starter functions -- A.9 Confidence intervals -- A.10 Prediction and inverse regression -- A.10.1 Effective dose -- A.10.2 Relative potency -- Appendix B: Dose-response model functions -- B.1 Log-logistic models -- B.1.1 Four-parameter log-logistic models -- B.1.1.1 Three-parameter version B.1.1.2 Two-parameter version -- B.1.1.3 E-max and Michaelis-Menten models -- B.1.2 Extensions -- B.1.2.1 Generalized log-logistic models -- B.1.2.2 A model with two slope parameters -- B.1.2.3 Hormesis models -- B.1.2.4 Two- and three-phase models -- B.1.2.5 Fractional polynomial models -- B.2 Log-normal models -- B.3 Weibull models -- B.3.1 Weibull type 1 models -- B.3.1.1 Exponential decay model -- B.3.1.2 Other special cases -- B.3.2 Weibull type 2 models -- B.3.2.1 Asymptotic regression -- B.3.2.2 Other special cases -- B.3.2.3 Generalized Weibull-2 model -- B.4 Other types of models -- B.4.1 Gamma models -- B.4.2 Multistage models -- B.4.3 NEC -- B.4.4 Biphasic models with a peak -- B.5 Fixing parameters -- Appendix C: R code for plots -- C.1 Continuous dose-response data -- C.1.1 Ferulic acid as an herbicide -- C.2 Estimation of BMD -- C.2.1 Pathogens in food -- C.2.2 Toxicity of an antituberculosis drug -- C.3 Hierarchical nonlinear models -- C.3.1 Inhibition of photosynthesis in spinach -- C.3.2 Herbicides with auxin effects -- C.3.3 Drought stress resistance in Brassica oleracea -- Bibliography -- Index |
| Title | Dose-Response Analysis Using R |
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