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ANOVA and Mixed Models A Short Introduction Using R

ANOVA and Mixed Models A Short Introduction Using R

ANOVA and Mixed Models: A Short Introduction Using R provides both the practitioner and researcher a compact introduction to the analysis of data from the most popular experimental designs. Based on knowledge from an introductory course on probability and statistics the theoretical foundations of the most important models are introduced. The focus is on an intuitive understanding of the theory common pitfalls in practice and the application of the methods in R. From data visualization and model fitting up to the interpretation of the corresponding output the whole workflow is presented using R. The book does not only cover standard ANOVA models but also models for more advanced designs and mixed models which are common in many practical applications. Features Accessible to readers with a basic background in probability and statistics Covers fundamental concepts of experimental design and cause-effect relationships Introduces classical ANOVA models including contrasts and multiple testing Provides an example-based introduction to mixed models Features basic concepts of split-plot and incomplete block designs R code available for all steps Supplementary website with additional resources and updates are available here. This book is primarily aimed at students researchers and practitioners from all areas who wish to analyze corresponding data with R. Readers will learn a broad array of models hand-in-hand with R including the applications of some of the most important add-on packages. | ANOVA and Mixed Models A Short Introduction Using R

GBP 49.99
1

Methods and Applications of Autonomous Experimentation

Methods and Applications of Autonomous Experimentation

Autonomous Experimentation is poised to revolutionize scientific experiments at advanced experimental facilities. Whereas previously human experimenters were burdened with the laborious task of overseeing each measurement recent advances in mathematics machine learning and algorithms have alleviated this burden by enabling automated and intelligent decision-making minimizing the need for human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experiences this book is a practical guide to successful Autonomous Experimentation. Despite the field’s growing potential there exists numerous myths and misconceptions surrounding Autonomous Experimentation. Combining insights from theorists machine-learning engineers and applied scientists this book aims to lay the foundation for future research and widespread adoption within the scientific community. This book is particularly useful for members of the scientific community looking to improve their research methods but also contains additional insights for students and industry professionals interested in the future of the field. | Methods and Applications of Autonomous Experimentation

GBP 69.99
1

Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies

Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies

Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields of reinforcement learning (RL) and cyber-physical systems (CPSs). Rooted in behavioral psychology RL is one of the primary strands of machine learning. Different from other machine learning algorithms such as supervised learning and unsupervised learning the key feature of RL is its unique learning paradigm i. e. trial-and-error. Combined with the deep neural networks deep RL become so powerful that many complicated systems can be automatically managed by AI agents at a superhuman level. On the other hand CPSs are envisioned to revolutionize our society in the near future. Such examples include the emerging smart buildings intelligent transportation and electric grids. However the conventional hand-programming controller in CPSs could neither handle the increasing complexity of the system nor automatically adapt itself to new situations that it has never encountered before. The problem of how to apply the existing deep RL algorithms or develop new RL algorithms to enable the real-time adaptive CPSs remains open. This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness of RL techniques. FeaturesIntroduces reinforcement learning including advanced topics in RL Applies reinforcement learning to cyber-physical systems and cybersecurity Contains state-of-the-art examples and exercises in each chapterProvides two cybersecurity case studies Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies is an ideal text for graduate students or junior/senior undergraduates in the fields of science engineering computer science or applied mathematics. It would also prove useful to researchers and engineers interested in cybersecurity RL and CPS. The only background knowledge required to appreciate the book is a basic knowledge of calculus and probability theory.

GBP 44.99
1

Method of Averaging for Differential Equations on an Infinite Interval Theory and Applications

Method of Averaging for Differential Equations on an Infinite Interval Theory and Applications

In recent years mathematicians have detailed simpler proofs of known theorems have identified new applications of the method of averaging and have obtained many new results of these applications. Encompassing these novel aspects Method of Averaging of the Infinite Interval: Theory and Applications rigorously explains the modern theory of the method of averaging and provides a solid understanding of the results obtained when applying this theory. The book starts with the less complicated theory of averaging linear differential equations (LDEs) focusing on almost periodic functions. It describes stability theory and Shtokalo's method and examines various applications including parametric resonance and the construction of asymptotics. After establishing this foundation the author goes on to explore nonlinear equations. He studies standard form systems in which the right-hand side of a system is proportional to a small parameter and proves theorems similar to Banfi's theorem. The final chapters are devoted to systems with a rapidly rotating phase. Covering an important asymptotic method of differential equations this book provides a thorough understanding of the method of averaging theory and its resulting applications. | Method of Averaging for Differential Equations on an Infinite Interval Theory and Applications

GBP 210.00
1

Real-World Evidence in Drug Development and Evaluation

Real-World Evidence in Drug Development and Evaluation

Real-world evidence (RWE) has been at the forefront of pharmaceutical innovations. It plays an important role in transforming drug development from a process aimed at meeting regulatory expectations to an operating model that leverages data from disparate sources to aid business regulatory and healthcare decision making. Despite its many benefits there is no single book systematically covering the latest development in the field. Written specifically for pharmaceutical practitioners Real-World Evidence in Drug Development and Evaluation presents a wide range of RWE applications throughout the lifecycle of drug product development. With contributions from experienced researchers in the pharmaceutical industry the book discusses at length RWE opportunities challenges and solutions. Features Provides the first book and a single source of information on RWE in drug development Covers a broad array of topics on outcomes- and value-based RWE assessments Demonstrates proper Bayesian application and causal inference for real-world data (RWD) Presents real-world use cases to illustrate the use of advanced analytics and statistical methods to generate insights Offers a balanced discussion of practical RWE issues at hand and technical solutions suitable for practitioners with limited data science expertise

GBP 44.99
1

Statistical Techniques for Data Analysis

Statistical Techniques for Data Analysis

Since the first edition of this book appeared computers have come to the aid of modern experimenters and data analysts bringing with them data analysis techniques that were once beyond the calculational reach of even professional statisticians. Today scientists in every field have access to the techniques and technology they need to analyze statistical data. All they need is practical guidance on how to use them. Valuable to everyone who produces uses or evaluates scientific data Statistical Techniques for Data Analysis Second Edition provides straightforward discussion of basic statistical techniques and computer analysis. The purpose structure and general principles of the book remain the same as the first edition but the treatment now includes updates in every chapter additional topics and most importantly an introduction to use of the MINITAB Statistical Software. The presentation of each technique includes motivation and discussion of the statistical analysis a hand-calculated example the same example calculated using MINITAB and discussion of the MINITAB output and conclusions. Highlights of the Second Edition:Detailed discussion and use of MINITAB in examples complete with code and outputA new chapter addressing proportions time to event data and time series data in the metrology settingAdditional material on hypothesis testingDiscussion of critical valuesA look at mistakes commonly made in data analysis

GBP 44.99
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Confidence Intervals for Discrete Data in Clinical Research

Confidence Intervals for Discrete Data in Clinical Research

Confidence Intervals for Discrete Data in Clinical Research is designed as a toolbox for biomedical researchers. Analysis of discrete data is one of the most used yet vexing areas in clinical research. The array of methodologies available in the literature to address the inferential questions for binomial and multinomial data can be a double-edged sword. On the one hand these methods open a rich avenue of exploration of data; on the other the wide-ranging and competing methodologies potentially lead to conflicting inferences adding to researchers' confusion and frustration and also leading to reporting bias. This book addresses the problems that many practitioners experience in choosing and implementing fit for purpose data analysis methods to answer critical inferential questions for binomial and count data. The book is an outgrowth of the authors' collective experience in biomedical research and provides an excellent overview of inferential questions of interest for binomial proportions and rates based on count data and reviews various solutions to these problems available in the literature. Each chapter discusses the strengths and weaknesses of the methods and suggests practical recommendations. The book's primary focus is on applications in clinical research and the goal is to provide direct benefit to the users involved in the biomedical field.

GBP 99.99
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Flexible Imputation of Missing Data Second Edition

Flexible Imputation of Missing Data Second Edition

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes like deletion or mean imputation only work under highly restrictive conditions which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data. | Flexible Imputation of Missing Data Second Edition

GBP 38.99
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Basics of Matrix Algebra for Statistics with R

Basics of Matrix Algebra for Statistics with R

A Thorough Guide to Elementary Matrix Algebra and Implementation in RBasics of Matrix Algebra for Statistics with R provides a guide to elementary matrix algebra sufficient for undertaking specialized courses such as multivariate data analysis and linear models. It also covers advanced topics such as generalized inverses of singular and rectangular matrices and manipulation of partitioned matrices for those who want to delve deeper into the subject. The book introduces the definition of a matrix and the basic rules of addition subtraction multiplication and inversion. Later topics include determinants calculation of eigenvectors and eigenvalues and differentiation of linear and quadratic forms with respect to vectors. The text explores how these concepts arise in statistical techniques including principal component analysis canonical correlation analysis and linear modeling. In addition to the algebraic manipulation of matrices the book presents numerical examples that illustrate how to perform calculations by hand and using R. Many theoretical and numerical exercises of varying levels of difficulty aid readers in assessing their knowledge of the material. Outline solutions at the back of the book enable readers to verify the techniques required and obtain numerical answers. Avoiding vector spaces and other advanced mathematics this book shows how to manipulate matrices and perform numerical calculations in R. It prepares readers for higher-level and specialized studies in statistics.

GBP 44.99
1

Fundamentals of Internet of Things

Fundamentals of Internet of Things

The Internet of Things (IoT) networks have revolutionized the world and have innumerable real-time applications on automation. A few examples include driverless cars remote monitoring of the elderly remote order of tea or coffee of your choice from a vending machine and home/industrial automation amongst others. Fundamentals of Internet of Things build the foundations of IoT networks by leveraging the relevant concepts from signal processing communications net-works and machine learning. The book covers two fundamental components of IoT networks namely the Internet and Things. In particular the book focuses on networking concepts protocols clustering data fusion localization energy harvesting control optimization data analytics fog computing privacy and security including elliptic curve cryptography and blockchain technology. Most of the existing books are theoretical and without many mathematical details and examples. In addition some essential topics of the IoT networks are also missing in the existing books. Features: • The book covers cutting-edge research topics• Provides mathematical understanding of the topics in addition to relevant theory and insights• Includes illustrations with hand-solved numerical examples for visualization of the theory and testing of understanding• Lucid and crisp explanation to lessen the study time of the reader The book is a complete package of the fundamentals of IoT networks and is suitable for graduate-level students and researchers who want to dive into the world of IoT networks.

GBP 145.00
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Basic Experimental Strategies and Data Analysis for Science and Engineering

Basic Experimental Strategies and Data Analysis for Science and Engineering

Every technical investigation involving trial-and-error experimentation embodies a strategy for deciding what experiments to perform when to quit and how to interpret the data. This handbook presents several statistically derived strategies which are more efficient than any intuitive approach and will get the investigator to their goal with the fewest experiments give the greatest degree of reliability to their conclusions and keep the risk of overlooking something of practical importance to a minimum. Features:Provides a comprehensive desk reference on experimental design that will be useful to practitioners without extensive statistical knowledgeFeatures a review of the necessary statistical prerequisitesPresents a set of tables that allow readers to quickly access various experimental designsIncludes a roadmap for where and when to use various experimental design strategiesShows compelling examples of each method discussedIllustrates how to reproduce results using several popular software packages on a supplementary websiteFollowing the outlines and examples in this book should quickly allow a working professional or student to select the appropriate experimental design for a research problem at hand follow the design to conduct the experiments and analyze and interpret the resulting data. John Lawson and John Erjavec have a combined 25 years of industrial experience and over 40 years of academic experience. They have taught this material to numerous practicing engineers and scientists as well as undergraduate and graduate students. | Basic Experimental Strategies and Data Analysis for Science and Engineering

GBP 44.99
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Introduction to Data Science Data Analysis and Prediction Algorithms with R

Introduction to Data Science Data Analysis and Prediction Algorithms with R

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability statistical inference linear regression and machine learning. It also helps you develop skills such as R programming data wrangling data visualization predictive algorithm building file organization with UNIX/Linux shell version control with Git and GitHub and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary although some experience with programming may be helpful. The book is divided into six parts: R data visualization statistics with R data wrangling machine learning and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state self-reported student heights trends in world health and economics the impact of vaccines on infectious disease rates the financial crisis of 2007-2008 election forecasting building a baseball team image processing of hand-written digits and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course. | Introduction to Data Science Data Analysis and Prediction Algorithms with R

GBP 82.99
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Mendelian Randomization Methods for Causal Inference Using Genetic Variants

Mendelian Randomization Methods for Causal Inference Using Genetic Variants

Mendelian Randomization: Methods For Causal Inference Using Genetic Variants provides thorough coverage of the methods and practical elements of Mendelian randomization analysis. It brings together diverse aspects of Mendelian randomization from the fields of epidemiology statistics genetics and bioinformatics. Through multiple examples the first part of the book introduces the reader to the concept of Mendelian randomization showing how to perform simple Mendelian randomization investigations and interpret the results. The second part of the book addresses specific methodological issues relevant to the practice of Mendelian randomization including robust methods weak instruments multivariable methods and power calculations. The authors present the theoretical aspects of these issues in an easy-to-understand way by using non-technical language. The last part of the book examines the potential for Mendelian randomization in the future exploring both methodological and applied developments. Features Offers first-hand in-depth guidance on Mendelian randomization from leaders in the field Makes the diverse aspects of Mendelian randomization understandable to newcomers Illustrates technical details using data from applied analyses Discusses possible future directions for research involving Mendelian randomization Software code is provided in the relevant chapters and is also available at the supplementary website This book gives epidemiologists statisticians geneticists and bioinformaticians the foundation to understand how to use genetic variants as instrumental variables in observational data. New in Second Edition: The second edition of the book has been substantially re-written to reduce the amount of technical content and emphasize practical consequences of theoretical issues. Extensive material on the use of two-sample Mendelian randomization and publicly-available summarized data has been added. The book now includes several real-world examples that show how Mendelian randomization can be used to address questions of disease aetiology target validation and drug development | Mendelian Randomization Methods for Causal Inference Using Genetic Variants

GBP 66.99
1

Introduction to Stochastic Level Crossing Techniques

Introduction to Stochastic Level Crossing Techniques

Introduction to Stochastic Level Crossing Techniques describes stochastic models and their analysis using the System Point Level Crossing method (abbreviated SPLC or LC). This involves deriving probability density functions (pdfs) or cumulative probability distribution functions (cdfs) of key random variables applying simple level-crossing limit theorems developed by the author. The pdfs and/or cdfs are used to specify operational characteristics about the stochastic model of interest. The chapters describe distinct stochastic models and associated key random variables in the models. For each model a figure of a typical sample path (realization i. e. tracing over time) of the key random variable is displayed. For each model an analytic (Volterra) integral equation for the stationary pdf of the key random variable is created−by inspection of the sample path using the simple LC limit theorems. This LC method bypasses a great deal of algebra usually required by other methods of analysis. The integral equations will be solved directly or computationally. This book is meant for students of mathematics management science engineering natural sciences and researchers who use applied probability. It will also be useful to technical workers in a range of professions. Key Features: A description of one representative stochastic model (e. g. a single-server M/G/1 queue; a multiple server M/M/c queue; an inventory system; etc. ) Construction of a typical sample path of the key random variable of interest (e. g. the virtual waiting time or workload in queues; the net on-hand inventory in inventory systems; etc. ) Statements of the simple LC theorems which connect the sample-path upcrossing and downcrossing rates across state-space levels to simple mathematical functions of the stationary pdf of the key random variable at those state-space levels Creation of (usually Volterra) integral equations for the stationary pdf of the key random variable by inspection of the sample path Direct analytic solution of the integral equations where feasible; or computational solutions of the integral equations Use of the derived stationary pdfs for obtaining operational characteristics of the model

GBP 120.00
1

Applied Meta-Analysis with R and Stata

Applied Meta-Analysis with R and Stata

Review of the First Edition: The authors strive to reduce theory to a minimum which makes it a self-learning text that is comprehensible for biologists physicians etc. who lack an advanced mathematics background. Unlike in many other textbooks R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses graphics and simulations directly relating to meta-analysis… A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs. —Journal of Applied Statistics Statistical Meta-Analysis with R and Stata Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What’s New in the Second Edition: Adds Stata programs along with the R programs for meta-analysis Updates all the statistical meta-analyses with R/Stata programs Covers fixed-effects and random-effects MA meta-regression MA with rare-event and MA-IPD vs MA-SS Adds five new chapters on multivariate MA publication bias missing data in MA MA in evaluating diagnostic accuracy and network MA Suitable as a graduate-level text for a meta-data analysis course the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health medical research governmental agencies and the pharmaceutical industry. | Applied Meta-Analysis with R and Stata

GBP 44.99
1

Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement An Applied Approach Using SAS & STATA

Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement An Applied Approach Using SAS & STATA

Economic evaluation has become an essential component of clinical trial design to show that new treatments and technologies offer value to payers in various healthcare systems. Although many books exist that address the theoretical or practical aspects of cost-effectiveness analysis this book differentiates itself from the competition by detailing how to apply health economic evaluation techniques in a clinical trial context from both academic and pharmaceutical/commercial perspectives. It also includes a special chapter for clinical trials in Cancer. Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement is not just about performing cost-effectiveness analyses. It also emphasizes the strategic importance of economic evaluation and offers guidance and advice on the complex factors at play before during and after an economic evaluation. Filled with detailed examples the book bridges the gap between applications of economic evaluation in industry (mainly pharmaceutical) and what students may learn in university courses. It provides readers with access to SAS and STATA code. In addition Windows-based software for sample size and value of information analysis is available free of charge—making it a valuable resource for students considering a career in this field or for those who simply wish to know more about applying economic evaluation techniques. The book includes coverage of trial design case report form design quality of life measures sample sizes submissions to regulatory authorities for reimbursement Markov models cohort models and decision trees. Examples and case studies are provided at the end of each chapter. Presenting first-hand insights into how economic evaluations are performed from a drug development perspective the book supplies readers with the foundation required to succeed in an environment where clinical trials and cost-effectiveness of new treatments are central. It also includes thought-provoking exercises for use in classroom and seminar discussions. | Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement An Applied Approach Using SAS & STATA

GBP 44.99
1

Statistical Approaches in Oncology Clinical Development Current Paradigm and Methodological Advancement

Statistical Approaches in Oncology Clinical Development Current Paradigm and Methodological Advancement

Statistical Approaches in Oncology Clinical Development : Current Paradigm and Methodological Advancement presents an overview of statistical considerations in oncology clinical trials both early and late phase of development. It illustrates how novel statistical methods can enrich the design and analysis of modern oncology trials. The authors include many relevant real life examples from the pharmaceutical industry and academia based on their first-hand experience. Along with relevant references the book highlights current regulatory views. The book covers all aspects of cancer clinical trial starting from early phase development. The early part of the book covers novel phase I dose escalation design exposure response analysis and innovative phase II design. This includes early development strategy for cancer immunotherapy trials. The contributors also emphasized the role of biomarker and modern era of precision medicine. The second part focuses on the late stage development. This includes the application of adaptive design safety analysis and quality of life (QoL) data analysis. The final part discusses current regulatory perspective and challenges. Features:Covers a wide spectrum of topics related to real-life statistical challenges in oncology clinical trials. Provides a comprehensive overview of novel statistical methods to improve trial design and statistical analysis. Detailed case studies illustrate the real life applications. Satrajit Roychoudhury is a Senior Director and a member of the Statistical Research and Innovation group in Pfizer Inc. Prior to joining; he was a member of Statistical Methodology and consulting group in Novartis. He has 11 years of extensive experience in working with different phases of clinical trial. His area of research includes early phase oncology trials survival analysis model informed drug development and use of Bayesian methods in clinical trials. He is industry co-chair for the ASA Biopharmaceutical Section Regulatory-Industry Workshop and has provided statistical training in major conferences including the Joint Statistical Meetings ASA Biopharmaceutical Section Regulatory-Industry Workshop and ICSA Applied Statistics Symposium. Soumi Lahiri has 12 years of extensive experience in working different therapeutic areas. She is the former Director of Biostatistics in Clinical Oncology GlaxoSmithKline. She has also worked in the oncology division of Novartis Pharmaceutical Company for two years. She is an active member of the ASA Biopharmaceutical section and former chair of the membership committee. | Statistical Approaches in Oncology Clinical Development Current Paradigm and Methodological Advancement

GBP 51.99
1