Dose-Response Analysis Using R
Dose-Response Analysis Using R

Author: Christian Ritz

Publisher: CRC Press

Published: 2019-07-19

Total Pages: 227

ISBN-13: 1351981048

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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.

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

Author: Dan Lin

Publisher: Springer Science & Business Media

Published: 2012-08-27

Total Pages: 285

ISBN-13: 3642240070

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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.

Nonlinear Regression with R
Nonlinear Regression with R

Author: Christian Ritz

Publisher: Springer Science & Business Media

Published: 2008-12-11

Total Pages: 151

ISBN-13: 0387096167

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- Coherent and unified treatment of nonlinear regression with R. - Example-based approach. - Wide area of application.

Dose-Response Analysis Using R
Dose-Response Analysis Using R

Author: Christian Ritz

Publisher: CRC Press

Published: 2019-07-19

Total Pages: 310

ISBN-13: 135198103X

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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.

Science and Decisions
Science and Decisions

Author: National Research Council

Publisher: National Academies Press

Published: 2009-03-24

Total Pages: 422

ISBN-13: 0309120462

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Risk assessment has become a dominant public policy tool for making choices, based on limited resources, to protect public health and the environment. It has been instrumental to the mission of the U.S. Environmental Protection Agency (EPA) as well as other federal agencies in evaluating public health concerns, informing regulatory and technological decisions, prioritizing research needs and funding, and in developing approaches for cost-benefit analysis. However, risk assessment is at a crossroads. Despite advances in the field, risk assessment faces a number of significant challenges including lengthy delays in making complex decisions; lack of data leading to significant uncertainty in risk assessments; and many chemicals in the marketplace that have not been evaluated and emerging agents requiring assessment. Science and Decisions makes practical scientific and technical recommendations to address these challenges. This book is a complement to the widely used 1983 National Academies book, Risk Assessment in the Federal Government (also known as the Red Book). The earlier book established a framework for the concepts and conduct of risk assessment that has been adopted by numerous expert committees, regulatory agencies, and public health institutions. The new book embeds these concepts within a broader framework for risk-based decision-making. Together, these are essential references for those working in the regulatory and public health fields.

10 Steps to Develop Great Learners
10 Steps to Develop Great Learners

Author: John Hattie

Publisher: Routledge

Published: 2022-04-07

Total Pages: 168

ISBN-13: 1000574164

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What can concerned parents and carers do to ensure their children, of all ages, develop great learning habits which will help them achieve their maximum at school and in life? This is probably one of the most important questions any parent can ask and now John Hattie, one of the most respected and renowned Education researchers in the world draws on his globally famous Visible Learning research to provide some answers. Writing this book with his own son Kyle, himself a respected teacher, the Hatties offer a 10-step plan to nurturing curiosity and intellectual ambition and providing a home environment that encourages and values learning. These simple steps based on the strongest of research evidence and packed full of practical advice can be followed by any parent or carer to support and enhance learning and maximize the potential of their children. Areas covered include: Communicating effectively with teachers Being the ‘first learner’ and demonstrating openness to new ideas and thinking Choosing the right school for your child Promoting the ‘language of learning’ Having appropriately high expectations and understanding the power of feedback Anyone concerned about the education and development of our children should read this book. For parents it is an essential guide that could make a vital difference to your child's life. For schools, school leaders and education authorities this is a book you should be encouraging every parent to read to support learning and maximize opportunities for all.

Drug Synergism and Dose-Effect Data Analysis
Drug Synergism and Dose-Effect Data Analysis

Author: Ronald J. Tallarida

Publisher: CRC Press

Published: 2000-07-21

Total Pages: 268

ISBN-13: 1420036106

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Not since this author's bestselling Manual of Pharmacologic Calculation has there been an available reference for drug data analysis. Incorporating the most relevant parts of that work, Drug Synergism and Dose-Effect Data Analysis focuses on drug combinations and all the quantitative analyses needed to analyze drug combination dose-effect data and to design experiments with two or more compounds. The book contains the statistical methods, the theory, and the computation algorithms needed to analyze single and combination drug data. Numerous examples accompany a presentation that illustrates the calculations and experimental design considerations for modern drug analysis.

Dose Finding in Drug Development
Dose Finding in Drug Development

Author: Naitee Ting

Publisher: Springer Science & Business Media

Published: 2006-12-29

Total Pages: 262

ISBN-13: 0387337067

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If you have ever wondered when visiting the pharmacy how the dosage of your prescription is determined this book will answer your questions. Dosing information on drug labels is based on discussion between the pharmaceutical manufacturer and the drug regulatory agency, and the label is a summary of results obtained from many scientific experiments. The book introduces the drug development process, the design and the analysis of clinical trials. Many of the discussions are based on applications of statistical methods in the design and analysis of dose response studies. Important procedural steps from a pharmaceutical industry perspective are also examined.

Applied Multivariate Statistics with R
Applied Multivariate Statistics with R

Author: Daniel Zelterman

Publisher: Springer Nature

Published: 2023-01-20

Total Pages: 469

ISBN-13: 3031130057

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Now in its second edition, this book brings multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source shareware program R, Dr. Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays; linear algebra; univariate, bivariate and multivariate normal distributions; factor methods; linear regression; discrimination and classification; clustering; time series models; and additional methods. He uses practical examples from diverse disciplines, to welcome readers from a variety of academic specialties. Each chapter includes exercises, real data sets, and R implementations. The book avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary. New to this edition are chapters devoted to longitudinal studies and the clustering of large data. It is an excellent resource for students of multivariate statistics, as well as practitioners in the health and life sciences who are looking to integrate statistics into their work.

Toxicological Effects of Methylmercury
Toxicological Effects of Methylmercury

Author: National Research Council

Publisher: National Academies Press

Published: 2000-09-27

Total Pages: 364

ISBN-13: 0309171717

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Mercury is widespread in our environment. Methylmercury, one organic form of mercury, can accumulate up the aquatic food chain and lead to high concentrations in predatory fish. When consumed by humans, contaminated fish represent a public health risk. Combustion processes, especially coal-fired power plants, are major sources of mercury contamination in the environment. The U.S. Environmental Protection Agency (EPA) is considering regulating mercury emissions from those plants. Toxicological Effects of Methylmercury reviews the health effects of methylmercury and discusses the estimation of mercury exposure from measured biomarkers, how differences between individuals affect mercury toxicity, and appropriate statistical methods for analysis of the data and thoroughly compares the epidemiological studies available on methylmercury. Included are discussions of current mercury levels on public health and a delineation of the scientific aspects and policy decisions involved in the regulation of mercury. This report is a valuable resource for individuals interested in the public health effects and regulation of mercury. The report also provides an excellent example of the implications of decisions in the risk assessment process for a larger audience.