Dose Response Analysis Using R

Dose Response Analysis Using R

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, ...

Author: Christian Ritz

Publisher: CRC Press

ISBN: 9781351981040

Category: Mathematics

Page: 214

View: 548

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.
Categories: Mathematics

Dose Response Analysis Using R

Dose Response Analysis Using R

critically important aspect of estimation in dose-response analysis is the choice of the so-called starting values for the model parameters. The numerical optimization procedure used for estimating the model parameters needs to be ...

Author: Christian Ritz

Publisher: CRC Press

ISBN: 9781351981033

Category: Mathematics

Page: 214

View: 990

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.
Categories: Mathematics

Modeling Dose Response Microarray Data in Early Drug Development Experiments Using R

Modeling Dose Response Microarray Data in Early Drug Development Experiments Using R

This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to ...

Author: Dan Lin

Publisher: Springer Science & Business Media

ISBN: 9783642240072

Category: Mathematics

Page: 282

View: 346

This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to analyze this data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students. Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as inference under order restrictions and non-linear parametric models, which are used in the second part of the book. Part II is the core of the book, in which we focus on the analysis of dose-response microarray data. Methodological topics discussed include: • Multiplicity adjustment • Test statistics and procedures for the analysis of dose-response microarray data • Resampling-based inference and use of the SAM method for small-variance genes in the data • Identification and classification of dose-response curve shapes • Clustering of order-restricted (but not necessarily monotone) dose-response profiles • Gene set analysis to facilitate the interpretation of microarray results • Hierarchical Bayesian models and Bayesian variable selection • Non-linear models for dose-response microarray data • Multiple contrast tests • Multiple confidence intervals for selected parameters adjusted for the false coverage-statement rate All methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.
Categories: Mathematics

Using R for Modelling and Quantitative Methods in Fisheries

Using R for Modelling and Quantitative Methods in Fisheries

Chapman & Hall/CRC The R Series Series Editors John M. Chambers, Department of Statistics, Stanford University, ... Second Edition Hadley Wickham Dose Response Analysis Using R Christian Ritz, Signe Marie Jensen, Daniel Gerhard, ...

Author: Malcolm Haddon

Publisher: CRC Press

ISBN: 9781000079272

Category: Technology & Engineering

Page: 338

View: 908

Using R for Modelling and Quantitative Methods in Fisheries has evolved and been adapted from an earlier book by the same author and provides a detailed introduction to analytical methods commonly used by fishery scientists, ecologists, and advanced students using the open-source software R as a programming tool. Some knowledge of R is assumed, as this is a book about using R, but an introduction to the development and working of functions, and how one can explore the contents of R functions and packages, is provided. The example analyses proceed step-by-step using code listed in the book and from the book’s companion R package, MQMF, available from GitHub and the standard archive, CRAN. The examples are designed to be simple to modify so the reader can quickly adapt the methods described to use with their own data. A primary aim of the book is to be a useful resource to natural resource practitioners and students. Featured Chapters: Model Parameter Estimation provides a detailed explanation of the requirements and steps involved in fitting models to data, using R and, mainly, maximum likelihood methods. On Uncertainty uses R to implement bootstrapping, likelihood profiles, asymptotic errors, and Bayesian posteriors to characterize any uncertainty in an analysis. The use of the Monte Carlo Markov Chain methodology is examined in some detail. Surplus Production Models applies all the methods examined in the earlier parts of the book to conducting a stock assessment. This included fitting alternative models to the available data, characterizing the uncertainty in different ways, and projecting the optimum models forward in time as the basis for providing useful management advice.
Categories: Technology & Engineering

Encyclopedia of Biopharmaceutical Statistics Four Volume Set

Encyclopedia of Biopharmaceutical Statistics   Four Volume Set

Clinical Trial Designs with Prospective Patient Population Enrichment by Response ... Clinical Trial Process Clinical Trial ... Clinical Trial : N - of - 1 Design Analysis . Clinical Trials . ... Dose Finding Clinical Trials Using R ..

Author: Shein-Chung Chow

Publisher: CRC Press

ISBN: 9781351110266

Category: Medical

Page: 2780

View: 886

Since the publication of the first edition in 2000, there has been an explosive growth of literature in biopharmaceutical research and development of new medicines. This encyclopedia (1) provides a comprehensive and unified presentation of designs and analyses used at different stages of the drug development process, (2) gives a well-balanced summary of current regulatory requirements, and (3) describes recently developed statistical methods in the pharmaceutical sciences. Features of the Fourth Edition: 1. 78 new and revised entries have been added for a total of 308 chapters and a fourth volume has been added to encompass the increased number of chapters. 2. Revised and updated entries reflect changes and recent developments in regulatory requirements for the drug review/approval process and statistical designs and methodologies. 3. Additional topics include multiple-stage adaptive trial design in clinical research, translational medicine, design and analysis of biosimilar drug development, big data analytics, and real world evidence for clinical research and development. 4. A table of contents organized by stages of biopharmaceutical development provides easy access to relevant topics. About the Editor: Shein-Chung Chow, Ph.D. is currently an Associate Director, Office of Biostatistics, U.S. Food and Drug Administration (FDA). Dr. Chow is an Adjunct Professor at Duke University School of Medicine, as well as Adjunct Professor at Duke-NUS, Singapore and North Carolina State University. Dr. Chow is the Editor-in-Chief of the Journal of Biopharmaceutical Statistics and the Chapman & Hall/CRC Biostatistics Book Series and the author of 28 books and over 300 methodology papers. He was elected Fellow of the American Statistical Association in 1995.
Categories: Medical

Encyclopedia of Quantitative Risk Analysis and Assessment

Encyclopedia of Quantitative Risk Analysis and Assessment

A qualitative dose–response model is defined through the alternative H1, e.g., a trend alternative H1 : F 0 “ ≤ ”F ... a dose–response model can be selected from standard models used for quantal doseresponse analysis [4]: Probit R(d) ...

Author:

Publisher: John Wiley & Sons

ISBN: 9780470035498

Category: Mathematics

Page: 2176

View: 716

Leading the way in this field, the Encyclopedia of Quantitative Risk Analysis and Assessment is the first publication to offer a modern, comprehensive and in-depth resource to the huge variety of disciplines involved. A truly international work, its coverage ranges across risk issues pertinent to life scientists, engineers, policy makers, healthcare professionals, the finance industry, the military and practising statisticians. Drawing on the expertise of world-renowned authors and editors in this field this title provides up-to-date material on drug safety, investment theory, public policy applications, transportation safety, public perception of risk, epidemiological risk, national defence and security, critical infrastructure, and program management. This major publication is easily accessible for all those involved in the field of risk assessment and analysis. For ease-of-use it is available in print and online.
Categories: Mathematics

Statistics in Toxicology Using R

Statistics in Toxicology Using R

Rarely, the objective of dose-response analysis is to identify the most likely dose-response model for interpretation purposes. One should be careful, remembering Box's comment: “all models are wrong, some are helpful”.

Author: Ludwig A. Hothorn

Publisher: CRC Press

ISBN: 9781498786751

Category: Mathematics

Page: 252

View: 212

The apparent contradiction between statistical significance and biological relevance has diminished the value of statistical methods as a whole in toxicology. Moreover, recommendations for statistical analysis are imprecise in most toxicological guidelines. Addressing these dilemmas, Statistics in Toxicology Using R explains the statistical analysi
Categories: Mathematics

Introduction to Environmental Toxicology

Introduction to Environmental Toxicology

Dose-response analysis using R. PLoS ONE 10(12): e0146021. doi:10.1371/journal.pone.0146021. Roberts, M.H. 1989. Comparison of several computer programs for probit analysis of dose related mortality data. In Aquatic Toxicology and ...

Author: Wayne Landis

Publisher: CRC Press

ISBN: 9781498750448

Category: Science

Page: 470

View: 582

The fifth edition includes new sections on the use of adverse outcome pathways, how climate change changes how we think about toxicology, and a new chapter on contaminants of emerging concern. Additional information is provided on the derivation of exposure-response curves to describe toxicity and they are compared to the use of hypothesis testing. The text is unified around the theme of describing the entire cause-effect pathway from the importance of chemical structure in determining exposure and interaction with receptors to the use of complex systems and hierarchical patch dynamic theory to describe effects to landscapes.
Categories: Science

Toxicological Risk Assessment and Multi System Health Impacts from Exposure

Toxicological Risk Assessment and Multi System Health Impacts from Exposure

Dose-response analysis using R. PLoS One 10, e0146021. Available from: https://doi.org/10.1371/ journal.pone.0146021. Schulte, P.M., 2014. What is environmental stress? Insights from fish living in a variable environment. J. Exp. Biol.

Author: Aristidis M. Tsatsakis

Publisher: Elsevier

ISBN: 9780323853583

Category: Medical

Page: 684

View: 496

Toxicological Risk Assessment and Multisystem Health Impacts From Exposure highlights the emerging problems of human and environmental health attributable to cumulative and multiple sources of long-term exposure to environmental toxicants. The book describes the cellular, biological, immunological, endocrinologic, genetic, and epigenetic effects of long-term exposure. It examines how the combined exposure to nanomaterials, metals, pharmaceuticals, multifrequency radiation, dietary mycotoxins, and pesticides accelerates ecotoxicity in humans, animals, plants, and the larger environment. The book goes on to also offer insights into mixture risk assessments, protocols for evaluating the risks, and how this information can serve the regulatory agencies in setting safer exposure limits. The book is a go-to resource for scientists and professionals in the field tackling the current and emerging trends in modern toxicology and risk assessment. • Bridges basic research with clinical, epidemiological, regulatory, and translational research, conveying both an introductory understanding and the latest developments in the field • Evaluates real-life human health risk assessment for long-term exposures to xenobiotic mixtures and the role they play in contributing to chronic disease • Discusses advances in predictive (in silico) toxicology tools and the benefits of using omics technologies in toxicology research
Categories: Medical

Weed Ecology and New Approaches for Management

Weed Ecology and New Approaches for Management

Raimondi, M.; Oliveira, J.R.; Constantin, J.; Rios, F.; Gemelli, A.; Raimondi, R. Dose-response curve to soil applied herbicides and susceptibility evaluation of different Amaranthus species using model identity.

Author: Anna Kocira

Publisher: MDPI

ISBN: 9783036515120

Category: Science

Page: 286

View: 808

Satisfying consumer needs through the production of healthy and nutritious agricultural products is a substantial challenge facing modern agriculture. However, agricultural production should be carried out with care for plant health, biological safety of products, and environmental safety while minimizing the risks to human health. Therefore, the implementation of agricultural practices while respecting these principles is very important for improving the quantity and quality of crops. Additionally, ecosystems have been altered as a result of human activities and climate change, resulting in the reduction of biodiversity and creation of new niches where pests can thrive. This is of particular importance in 2020, as the United Nations General Assembly declared this year as the International Year of Plant Health (IYPH), with “protecting plants, protecting life” as a leading subject. This Special Issue promotes the subject of plant health and emphasize the importance of preventing the spread of pests, including weeds, which cause substantial economic losses. Research articles cover topics related to the biology and harmfulness of weeds, particularly in connection with crop health, segetal weed communities and their biodiversity, and integrated methods of weed control. For this Special Issue, we welcome all types of articles, including original research, opinions, and reviews.
Categories: Science