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Drug Development for Rare Diseases

Drug Development for Rare Diseases

A disease is defined as rare if the prevalence is fewer than 200 000 in the United States. It is estimated that there are more than 7 000 rare diseases which collectively affect 30 million Americans or 10% of the US population. This diverse and complex disease area poses challenges for patients caregivers regulators drug developers and other stakeholders. This book is proposed to give an overview of the common issues facing rare disease drug developers summarize challenges specific to clinical development in small populations discuss drug development strategies in the evolving regulatory environment explain generation and utilization of different data and evidence inside and beyond clinical trials and use recent examples to demonstrate these challenges and the development strategies that respond to the challenges. Key Features: • Rare disease. • Drug development. • Innovative clinical trial design. • Regulatory approval. • Real-world evidence. | Drug Development for Rare Diseases

GBP 120.00
1

Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

In engineering and quality control various situations including process validation and design verification require equivalence and noninferiority tests. Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers presents methods for using validation and verification test data to demonstrate equivalence and noninferiority in engineering and applied science. The book covers numerous tests drawn from the author’s more than 30 years of work in a range of industrial settings. It provides computational formulas for the tests methods to determine or justify sample sizes and formulas to calculate power and operating characteristic curves. The methods are accessible using standard statistical software and do not require complicated programming. The book also includes computer code and screen shots for SAS R and JMP. This book provides you with a guide to performing validation and verification tests that demonstrate the adequacy of your process system or product. It will help you choose the best test for your application.

GBP 59.99
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Handbook of Computational Group Theory

Handbook of Computational Group Theory

The origins of computation group theory (CGT) date back to the late 19th and early 20th centuries. Since then the field has flourished particularly during the past 30 to 40 years and today it remains a lively and active branch of mathematics. The Handbook of Computational Group Theory offers the first complete treatment of all the fundamental methods and algorithms in CGT presented at a level accessible even to advanced undergraduate students. It develops the theory of algorithms in full detail and highlights the connections between the different aspects of CGT and other areas of computer algebra. While acknowledging the importance of the complexity analysis of CGT algorithms the authors' primary focus is on algorithms that perform well in practice rather than on those with the best theoretical complexity. Throughout the book applications of all the key topics and algorithms to areas both within and outside of mathematics demonstrate how CGT fits into the wider world of mathematics and science. The authors include detailed pseudocode for all of the fundamental algorithms and provide detailed worked examples that bring the theorems and algorithms to life.

GBP 44.99
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Community College Mathematics Past Present and Future

Community College Mathematics Past Present and Future

This book explores the rich history of community college math with a specific focus on gatekeeper math classes. Gatekeeper math classes include courses such as college algebra introduction to statistics and all developmental math classes. For community colleges successful completion of these classes is imperative for student retention. This book presents a decade-by-decade analysis of the history of community college mathematics. The author employs a mix of conceptual empirical and quantitative research. The empirical research stems from interviews with 30 community college faculty members from seven community colleges. From the 1970s to the pandemic in the early 2020s the book explores math curricula as well as trends initiatives teaching practices and mandates that have impacted community college math. The positives and negatives of such trends initiatives and mandates are presented along with suggestions on how to apply such knowledge going forward. The author addresses the key questions: How can we build a future model for community college gatekeeper math classes that is both successful and sustainable? Additionally how can we learn from the past and the present to build such a model? This book will be ideal for students in graduate programs focusing on community college leadership or developmental education leadership as well as all those hoping to improve success rates in community college mathematics programs. | Community College Mathematics Past Present and Future

GBP 24.99
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Transfer Learning through Embedding Spaces

Transfer Learning through Embedding Spaces

Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated. This success however is conditioned on availability of huge annotated datasets to training AI models. Data annotation is a time-consuming and expensive task which still is being performed by human workers. Learning efficiently from less data is a next step for making AI more similar to natural intelligence. Transfer learning has been suggested a remedy to relax the need for data annotation. The core idea in transfer learning is to transfer knowledge across similar tasks and use similarities and previously learned knowledge to learn more efficiently. In this book we provide a brief background on transfer learning and then focus on the idea of transferring knowledge through intermediate embedding spaces. The idea is to couple and relate different learning through embedding spaces that encode task-level relations and similarities. We cover various machine learning scenarios and demonstrate that this idea can be used to overcome challenges of zero-shot learning few-shot learning domain adaptation continual learning lifelong learning and collaborative learning. | Transfer Learning through Embedding Spaces

GBP 44.99
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Clinical Trial Data Analysis Using R and SAS

Clinical Trial Data Analysis Using R and SAS

Review of the First EditionThe goal of this book as stated by the authors is to fill the knowledge gap that exists between developed statistical methods and the applications of these methods. Overall this book achieves the goal successfully and does a nice job. I would highly recommend it …The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods. —Journal of Statistical SoftwareClinical Trial Data Analysis Using R and SAS Second Edition provides a thorough presentation of biostatistical analyses of clinical trial data with step-by-step implementations using R and SAS. The book’s practical detailed approach draws on the authors’ 30 years’ experience in biostatistical research and clinical development. The authors develop step-by-step analysis code using appropriate R packages and functions and SAS PROCS which enables readers to gain an understanding of the analysis methods and R and SAS implementation so that they can use these two popular software packages to analyze their own clinical trial data. What’s New in the Second EditionAdds SAS programs along with the R programs for clinical trial data analysis. Updates all the statistical analysis with updated R packages. Includes correlated data analysis with multivariate analysis of variance. Applies R and SAS to clinical trial data from hypertension duodenal ulcer beta blockers familial andenomatous polyposis and breast cancer trials. Covers the biostatistical aspects of various clinical trials including treatment comparisons time-to-event endpoints longitudinal clinical trials and bioequivalence trials.

GBP 44.99
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Logistic Regression Models

Logistic Regression Models

Logistic Regression Models presents an overview of the full range of logistic models including binary proportional ordered partially ordered and unordered categorical response regression procedures. Other topics discussed include panel survey skewed penalized and exact logistic models. The text illustrates how to apply the various models to health environmental physical and social science data. Examples illustrate successful modelingThe text first provides basic terminology and concepts before explaining the foremost methods of estimation (maximum likelihood and IRLS) appropriate for logistic models. It then presents an in-depth discussion of related terminology and examines logistic regression model development and interpretation of the results. After focusing on the construction and interpretation of various interactions the author evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers binomial logistic regression varieties of overdispersion and a number of extensions to the basic binary and binomial logistic model. Both real and simulated data are used to explain and test the concepts involved. The appendices give an overview of marginal effects and discrete change as well as a 30-page tutorial on using Stata commands related to the examples used in the text. Stata is used for most examples while R is provided at the end of the chapters to replicate examples in the text. Apply the models to your own dataData files for examples and questions used in the text as well as code for user-authored commands are provided on the book’s website formatted in Stata R Excel SAS SPSS and Limdep. See Professor Hilbe discuss the book.

GBP 52.99
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Clinical Trial Methodology

Clinical Trial Methodology

Now viewed as its own scientific discipline clinical trial methodology encompasses the methods required for the protection of participants in a clinical trial and the methods necessary to provide a valid inference about the objective of the trial. Drawing from the authors’ courses on the subject as well as the first author’s more than 30 years working in the pharmaceutical industry Clinical Trial Methodology emphasizes the importance of statistical thinking in clinical research and presents the methodology as a key component of clinical research. From ethical issues and sample size considerations to adaptive design procedures and statistical analysis the book first covers the methodology that spans every clinical trial regardless of the area of application. Crucial to the generic drug industry bioequivalence clinical trials are then discussed. The authors describe a parallel bioequivalence clinical trial of six formulations incorporating group sequential procedures that permit sample size re-estimation. The final chapters incorporate real-world case studies of clinical trials from the authors’ own experiences. These examples include a landmark Phase III clinical trial involving the treatment of duodenal ulcers and Phase III clinical trials that contributed to the first drug approved for the treatment of Alzheimer’s disease. Aided by the U. S. FDA the U. S. National Institutes of Health the pharmaceutical industry and academia the area of clinical trial methodology has evolved over the last six decades into a scientific discipline. This guide explores the processes essential for developing and conducting a quality clinical trial protocol and providing quality data collection biostatistical analyses and a clinical study report all while maintaining the highest standards of ethics and excellence.

GBP 44.99
1

Statistical Simulation Power Method Polynomials and Other Transformations

Statistical Simulation Power Method Polynomials and Other Transformations

Although power method polynomials based on the standard normal distributions have been used in many different contexts for the past 30 years it was not until recently that the probability density function (pdf) and cumulative distribution function (cdf) were derived and made available. Focusing on both univariate and multivariate nonnormal data generation Statistical Simulation: Power Method Polynomials and Other Transformations presents techniques for conducting a Monte Carlo simulation study. It shows how to use power method polynomials for simulating univariate and multivariate nonnormal distributions with specified cumulants and correlation matrices. The book first explores the methodology underlying the power method before demonstrating this method through examples of standard normal logistic and uniform power method pdfs. It also discusses methods for improving the performance of a simulation based on power method polynomials. The book then develops simulation procedures for systems of linear statistical models intraclass correlation coefficients and correlated continuous variates and ranks. Numerical examples and results from Monte Carlo simulations illustrate these procedures. The final chapter describes how the g-and-h and generalized lambda distribution (GLD) transformations are special applications of the more general multivariate nonnormal data generation approach. Throughout the text the author employs Mathematica® in a range of procedures and offers the source code for download online. Written by a longtime researcher of the power method this book explains how to simulate nonnormal distributions via easy-to-use power method polynomials. By using the methodology and techniques developed in the text readers can evaluate different transformations in terms of comparing percentiles measures of central tendency goodness-of-fit tests and more. | Statistical Simulation Power Method Polynomials and Other Transformations

GBP 64.99
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Questioning the Universe Concepts in Physics

Questioning the Universe Concepts in Physics

WINNER 2009 CHOICE AWARD OUTSTANDING ACADEMIC TITLE! The typical introduction to physics leaves readers with the impression that physics is about 30 different unconnected topics such as motion forces gravity electricity light heat energy and atoms. More often than not these readers are left to conclude that physics is mostly about boring lifeless numbers. Questioning the Universe: Concepts in Physics offers the nonscientist an alternative view: one that demonstrates how physics is perpetually evolving and shows how so many seemingly diverse concepts are intimately connected. In fact one could argue that the most important ideas in modern physics are all about unification and that these ideas are as fascinating as they are elegant. Physicists today believe that Mother Nature is remarkably efficient and requires only a relatively small number of laws to keep her universe in working order. We may not yet know all of these laws; but at the center of physics is a faith that she is indeed understandableand that someday we will see her full beauty. The purpose of this book is to tell readers the story of what we have learned about nature so far and how we have done it. Written to arouse curiosity this compelling and readable work: Delves into the most basic laws regarding motion and energy waves and particles Introduces modern theories including relativity quantum mechanics and particle physics Describes the key role played by that elemental building block the atom Discusses the evolution of the universe including the formation of stars and the mystery of dark matter and dark energy This book is not for those doing physics but is aimed at those who simply want to learn about physics so it requires only the most minimal math. What it | Questioning the Universe Concepts in Physics

GBP 175.00
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Student Solutions Manual for Gallian's Contemporary Abstract Algebra

Student Solutions Manual for Gallian's Contemporary Abstract Algebra

Whereas many partial solutions and sketches for the odd-numbered exercises appear in the book the Student Solutions Manual written by the author has comprehensive solutions for all odd-numbered exercises and large number of even-numbered exercises. This Manual also offers many alternative solutions to those appearing in the text. These will provide the student with a better understanding of the material. This is the only available student solutions manual prepared by the author of Contemporary Abstract Algebra Tenth Edition and is designed to supplement that text. Table of Contents Integers and Equivalence Relations0. Preliminaries Groups1. Introduction to Groups 2. Groups 3. Finite Groups; Subgroups 4. Cyclic Groups 5. Permutation Groups 6. Isomorphisms 7. Cosets and Lagrange's Theorem 8. External Direct Products 9. Normal Subgroups and Factor Groups 10. Group Homomorphisms 11. Fundamental Theorem of Finite Abelian Groups Rings12. Introduction to Rings 13. Integral Domains14. Ideals and Factor Rings 15. Ring Homomorphisms 16. Polynomial Rings 17. Factorization of Polynomials 18. Divisibility in Integral Domains FieldsFields19. Extension Fields 20. Algebraic Extensions21. Finite Fields 22. Geometric Constructions Special Topics23. Sylow Theorems 24. Finite Simple Groups 25. Generators and Relations 26. Symmetry Groups 27. Symmetry and Counting 28. Cayley Digraphs of Groups 29. Introduction to Algebraic Coding Theory 30. An Introduction to Galois Theory 31. Cyclotomic Extensions Biography Joseph A. Gallian earned his PhD from Notre Dame. In addition to receiving numerous national awards for his teaching and exposition he has served terms as the Second Vice President and the President of the MAA. He has served on 40 national committees chairing ten of them. He has published over 100 articles and authored six books. Numerous articles about his work have appeared in the national news outlets including the New York Times the Washington Post the Boston Globe and Newsweek among many others. | Student Solutions Manual for Gallian's Contemporary Abstract Algebra

GBP 44.99
1

Bayesian Applications in Pharmaceutical Development

Bayesian Applications in Pharmaceutical Development

The cost for bringing new medicine from discovery to market has nearly doubled in the last decade and has now reached $2. 6 billion. There is an urgent need to make drug development less time-consuming and less costly. Innovative trial designs/ analyses such as the Bayesian approach are essential to meet this need. This book will be the first to provide comprehensive coverage of Bayesian applications across the span of drug development from discovery to clinical trial to manufacturing with practical examples. This book will have a wide appeal to statisticians scientists and physicians working in drug development who are motivated to accelerate and streamline the drug development process as well as students who aspire to work in this field. The advantages of this book are: Provides motivating worked practical case examples with easy to grasp models technical details and computational codes to run the analyses Balances practical examples with best practices on trial simulation and reporting as well as regulatory perspectives Chapters written by authors who are individual contributors in their respective topics Dr. Mani Lakshminarayanan is a researcher and statistical consultant with more than 30 years of experience in the pharmaceutical industry. He has published over 50 articles technical reports and book chapters besides serving as a referee for several journals. He has a PhD in Statistics from Southern Methodist University Dallas Texas and is a Fellow of the American Statistical Association. Dr. Fanni Natanegara has over 15 years of pharmaceutical experience and is currently Principal Research Scientist and Group Leader for the Early Phase Neuroscience Statistics team at Eli Lilly and Company. She played a key role in the Advanced Analytics team to provide Bayesian education and statistical consultation at Eli Lilly. Dr. Natanegara is the chair of the cross industry-regulatory-academic DIA BSWG to ensure that Bayesian methods are appropriately utilized for design and analysis throughout the drug-development process. | Bayesian Applications in Pharmaceutical Development

GBP 44.99
1

Project-Based R Companion to Introductory Statistics

Project-Based R Companion to Introductory Statistics

Project-Based R Companion to Introductory Statistics is envisioned as a companion to a traditional statistics or biostatistics textbook with each chapter covering traditional topics such as descriptive statistics regression and hypothesis testing. However unlike a traditional textbook each chapter will present its material using a complete step-by-step analysis of a real publicly available dataset with an emphasis on the practical skills of testing assumptions data exploration and forming conclusions. The chapters in the main body of the book include a worked example showing the R code used at each step followed by a multi-part project for students to complete. These projects which could serve as alternatives to traditional discrete homework problems will illustrate how to put the pieces together and conduct a complete start-to-finish data analysis using the R statistical software package. At the end of the book there are several projects that require the use of multiple statistical techniques that could be used as a take-home final exam or final project for a class. Key features of the text: Organized in chapters focusing on the same topics found in typical introductory statistics textbooks (descriptive statistics regression two-way tables hypothesis testing for means and proportions etc. ) so instructors can easily pair this supplementary material with course plans Includes student projects for each chapter which can be assigned as laboratory exercises or homework assignments to supplement traditional homework Features real-world datasets from scientific publications in the fields of history pop culture business medicine and forensics for students to analyze Allows students to gain experience working through a variety of statistical analyses from start to finish The book is written at the undergraduate level to be used in an introductory statistical methods course or subject-specific research methods course such as biostatistics or research methods for psychology or business analytics. Author After a 10-year career as a research biostatistician in the Department of Ophthalmology and Visual Sciences at the University of Wisconsin-Madison Chelsea Myers teaches statistics and biostatistics at Rollins College and Valencia College in Central Florida. She has authored or co-authored more than 30 scientific papers and presentations and is the creator of the MCAT preparation website MCATMath. com.

GBP 48.99
1

Discovering Computer Science Interdisciplinary Problems Principles and Python Programming

Discovering Computer Science Interdisciplinary Problems Principles and Python Programming

Havill's problem-driven approach introduces algorithmic concepts in context and motivates students with a wide range of interests and backgrounds. Janet Davis Associate Professor and Microsoft Chair of Computer Science Whitman College This book looks really great and takes exactly the approach I think should be used for a CS 1 course. I think it really fills a need in the textbook landscape. Marie desJardins Dean of the College of Organizational Computational and Information Sciences Simmons University Discovering Computer Science is a refreshing departure from introductory programming texts offering students a much more sincere introduction to the breadth and complexity of this ever-growing field. James Deverick Senior Lecturer The College of William and Mary This unique introduction to the science of computing guides students through broad and universal approaches to problem solving in a variety of contexts and their ultimate implementation as computer programs. Daniel Kaplan DeWitt Wallace Professor Macalester College Discovering Computer Science: Interdisciplinary Problems Principles and Python Programming is a problem-oriented introduction to computational problem solving and programming in Python appropriate for a first course for computer science majors a more targeted disciplinary computing course or at a slower pace any introductory computer science course for a general audience. Realizing that an organization around language features only resonates with a narrow audience this textbook instead connects programming to students’ prior interests using a range of authentic problems from the natural and social sciences and the digital humanities. The presentation begins with an introduction to the problem-solving process contextualizing programming as an essential component. Then as the book progresses each chapter guides students through solutions to increasingly complex problems using a spiral approach to introduce Python language features. The text also places programming in the context of fundamental computer science principles such as abstraction efficiency testing and algorithmic techniques offering glimpses of topics that are traditionally put off until later courses. This book contains 30 well-developed independent projects that encourage students to explore questions across disciplinary boundaries over 750 homework exercises and 300 integrated reflection questions engage students in problem solving and active reading. The accompanying website — https://www. discoveringcs. net — includes more advanced content solutions to selected exercises sample code and data files and pointers for further exploration. | Discovering Computer Science Interdisciplinary Problems Principles and Python Programming

GBP 74.99
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Artificial Intelligence and the Two Singularities

Artificial Intelligence and the Two Singularities

The science of AI was born a little over 60 years ago but for most of that time its achievements were modest. In 2012 it experienced a big bang when a branch of statistics called Machine Learning (and a sub-branch called Deep Learning) was applied to it. Now machines have surpassed humans in image recognition and they are catching up with us at speech recognition and natural language processing. Every day the media reports the launch of a new service a new product and a new demonstration powered by AI. When will it end? The surprising truth is the AI revolution has only just begun. Artificial Intelligence and the Two Singularities argues that in the course of this century the exponential growth in the capability of AI is likely to bring about two singularities - points at which conditions are so extreme that the normal rules break down. The first is the economic singularity when machine skill reaches a level that renders many of us unemployable and requires an overhaul of our current economic and social systems. The second is the technological singularity when machine intelligence reaches and then surpasses the cognitive abilities of an adult human relegating us to the second smartest species on the planet. These singularities will present huge challenges but this book argues that we can meet these challenges and overcome them. If we do the rewards could be almost unimaginable. This book covers: • Recent developments in AI and its future potential • The economic singularity and the technological singularity in depth • The risks and opportunities presented by AI • What actions we should take Artificial intelligence can turn out to be the best thing ever to happen to humanity making our future wonderful almost beyond imagination. But only if we address head-on the challenges that it will raise. Calum Chace is a best-selling author of fiction and non-fiction books and articles focusing on the subject of artificial intelligence. He is a regular speaker on artificial intelligence and related technologies and runs a blog on the subject at www. pandoras-brain. com. Prior to becoming a full-time writer and speaker he spent 30 years in business as a marketer a strategy consultant and a CEO. He studied philosophy at Oxford University where he discovered that the science fiction he had been reading since boyhood was simply philosophy in fancy dress.

GBP 46.99
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