LEADER 03388oam 2200505Mn 450 001 9910793750303321 005 20190820011823.0 010 $a1-351-98104-8 010 $a1-351-98103-X 010 $a1-315-27009-9 035 $a(CKB)4100000008871992 035 $a(MiAaPQ)EBC5846300 035 $a(OCoLC)1109972695$z(OCoLC)1111977079 035 $a(OCoLC-P)1109972695 035 $a(FlBoTFG)9781315270098 035 $a(EXLCZ)994100000008871992 100 $a20190725d2018 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDose-response Analysis With R$b[electronic resource] 210 $cChapman & Hall$d2018 215 $a1 online resource (227 pages) 311 $a1-138-03431-2 320 $aIncludes bibliographical references and index. 330 $aNowadays 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. 606 $aDrugs$xDose-response relationship 606 $aDrugs$xTesting$xComputer simulation 606 $aR (Computer program language) 615 0$aDrugs$xDose-response relationship. 615 0$aDrugs$xTesting$xComputer simulation. 615 0$aR (Computer program language). 676 $a615.1 700 $aRitz$b Christian$0513581 701 $aStreibig$b Jens C$0513582 701 $aJensen$b Signe Marie$01528170 701 $aGerhard$b Daniel$01528171 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910793750303321 996 $aDose-response Analysis With R$93771598 997 $aUNINA