portada Dose-Response Analysis Using R (en Inglés)

Dose-Response Analysis Using R (en Inglés)

Formato

Libro Físico

Autor

Editorial

Año

2019

Idioma

Inglés

N° páginas

226

Encuadernación

Tapa Dura

Dimensiones

23.9x16x1.8 cm

Peso

0.50 kg.

ISBN13

9781138034310

N° edición

Categorías

Toxicología médica
Farmacia/prescripción/dispensación
Probabilidad y estadística

Dose-Response Analysis Using R (en Inglés)

Christian Ritz (Autor) · Signe Marie Jensen (Autor) · Daniel Gerhard (Autor) · CRC Press · Tapa Dura

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Reseña del libro "Dose-Response Analysis Using R (en Inglés)"

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.