Methods and Applications of Statistics in Clinical Trials,Volume 2: Planning, Analysis, and Inferential Methods includesupdates of established literature from the Wiley Encyclopedia ofClinical Trials as well as original material based on the latestdevelopments in clinical trials. Prepared by a leading expert, thesecond volume includes numerous contributions from currentprominent experts in the field of medical research. In addition,the volume features: • Multiple new articles exploring emerging topics, such asevaluation methods with threshold, empirical likelihood methods,nonparametric ROC analysis, over- and under-dispersed models, andmulti-armed bandit problems • Up-to-date research on the Cox proportional hazardmodel, frailty models, trial reports, intrarater reliability,conditional power, and the kappa index • Key qualitative issues including cost-effectivenessanalysis, publication bias, and regulatory issues, which arecrucial to the planning and data management of clinical trials
Facts101 is your complete guide to Methods and Applications of Statistics in Clinical Trials, Volume 2 - Planning, Analysis, and Inferential Methods. In this book, you will learn topics such as as those in your book plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.
A complete guide to the key statistical concepts essential for the design and construction of clinical trials As the newest major resource in the field of medical research, Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs presents a timely and authoritative reviewof the central statistical concepts used to build clinical trials that obtain the best results. The referenceunveils modern approaches vital to understanding, creating, and evaluating data obtained throughoutthe various stages of clinical trial design and analysis. Accessible and comprehensive, the first volume in a two-part set includes newly-written articles as well as established literature from the Wiley Encyclopedia of Clinical Trials. Illustrating a variety of statistical concepts and principles such as longitudinal data, missing data, covariates, biased-coin randomization, repeated measurements, and simple randomization, the book also provides in-depth coverage of the various trial designs found within phase I-IV trials. Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs also features: Detailed chapters on the type of trial designs, such as adaptive, crossover, group-randomized, multicenter, non-inferiority, non-randomized, open-labeled, preference, prevention, and superiority trials Over 100 contributions from leading academics, researchers, and practitioners An exploration of ongoing, cutting-edge clinical trials on early cancer and heart disease, mother-to-child human immunodeficiency virus transmission trials, and the AIDS Clinical Trials Group Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs is an excellent reference for researchers, practitioners, and students in the fields of clinicaltrials, pharmaceutics, biostatistics, medical research design, biology, biomedicine, epidemiology,and public health.
Classic biostatistics, a branch of statistical science, has as its main focus the applications of statistics in public health, the life sciences, and the pharmaceutical industry. Modern biostatistics, beyond just a simple application of statistics, is a confluence of statistics and knowledge of multiple intertwined fields. The application demands, the advancements in computer technology, and the rapid growth of life science data (e.g., genomics data) have promoted the formation of modern biostatistics. There are at least three characteristics of modern biostatistics: (1) in-depth engagement in the application fields that require penetration of knowledge across several fields, (2) high-level complexity of data because they are longitudinal, incomplete, or latent because they are heterogeneous due to a mixture of data or experiment types, because of high-dimensionality, which may make meaningful reduction impossible, or because of extremely small or large size; and (3) dynamics, the speed of development in methodology and analyses, has to match the fast growth of data with a constantly changing face. This book is written for researchers, biostatisticians/statisticians, and scientists who are interested in quantitative analyses. The goal is to introduce modern methods in biostatistics and help researchers and students quickly grasp key concepts and methods. Many methods can solve the same problem and many problems can be solved by the same method, which becomes apparent when those topics are discussed in this single volume.
The book aims to provide both comprehensive reviews of the classical methods and an introduction to new developments in medical statistics. The topics range from meta analysis, clinical trial design, causal inference, personalized medicine to machine learning and next generation sequence analysis. Since the publication of the first edition, there have been tremendous advances in biostatistics and bioinformatics. The new edition tries to cover as many important emerging areas and reflect as much progress as possible. Many distinguished scholars, who greatly advanced their research areas in statistical methodology as well as practical applications, also have revised several chapters with relevant updates and written new ones from scratch. The new edition has been divided into four sections, including, Statistical Methods in Medicine and Epidemiology, Statistical Methods in Clinical Trials, Statistical Genetics, and General Methods. To reflect the rise of modern statistical genetics as one of the most fertile research areas since the publication of the first edition, the brand new section on Statistical Genetics includes entirely new chapters reflecting the state of the art in the field. Although tightly related, all the book chapters are self-contained and can be read independently. The book chapters intend to provide a convenient launch pad for readers interested in learning a specific topic, applying the related statistical methods in their scientific research and seeking the newest references for in-depth research. Contents:Statistics in Medicine and Epidemiology:History of Statistical Thinking in Medicine (Tar Timothy Chen)Describing Data, Modeling Variation, and Statistical Practice (Hongyan Du and Ming T Tan)Covariate-Specific and Covariate-Adjusted Predictive Values of Prognostic Biomarkers with Survival Outcome (Yunbei Ma, Xiao-Hua Zhou and Kwun Chuen (Gary) Chan)Statistical Methods for Personalized Medicine (Lu Tian and Xiaoguang Zhao)Statistics Used in Quality Control, Quality Assurance, and Quality Improvement in Radiological Studies (Ying Lu and Shoujun Zhao)Applications of Statistical Methods in Medical Imaging (Jesse S Jin)Cost-Effectiveness Analysis and Evidence-Based Medicine (Jianli Li)Quality of Life: Issues Concerning Assessment and Analysis (Jiqian Fang and Yuantao Hao)Meta-Analysis (Xuyu Zhou, Jiqian Fang, Chuanhua Yu, Zongli Xu, Lu Tian, and Ying Lu)Statistical Models and Methods in Infectious Diseases (Hulin Wu and Shoujun Zhao)Special Models for Sampling Survey (Sujuan Gao)The Use of Capture–Recapture Methodology in Epidemiological Surveillance and Ecological Surveys (Anne Chao, T C Hsieh and Hsin-Chou Yang)Statistical Methods in the Effective Evaluation of Mass Screening for Diseases (Qing Liu)Statistics in Clinical Trials:Statistics in Biopharmaceutical Research and Development (Shein-Chung Chow and Annpey Pong)Statistics in Pharmacology and Pre-Clinical Studies (Tze Leung Lai, Mei-Chiung Shin and Guangrui Zhu)Statistics in Toxicology (James J Chen)Dose-Response Modeling and Benchmark Doses in Health Risk Assessment (Yiliang Zhu)Some Fundamental Statistical Issues and Methodologies in Confirmatory Trials (George Y H Chi, Haiyan Xu and Qing Liu)Surrogates for Qualitative Evaluation of Treatment Effects (Zhi Geng)Adaptive Trial Design in Clinical Research (Annpey Pong and Shein-Chung Chow)Statistics in the Research of Traditional Chinese Medicine (Danhui Yi and Yang Li)Statistical Genetics:Sparse Segment Identifications with Applications to DNA Copy Number Variation Analysis (X Jessie Jeng, T Tony Cai and Hongzhe Li)Statistical Methods for Design and Analysis of Linkage Studies (Qizhai Li, Hong Qin, Zhaohai Li, and Gang Zheng)Transcriptome Analysis Using Next-Generation Sequencing (Jingyi Jessica Li, Haiyan Huang, Minping Qian and Xuegong Zhang)Genetic Structure of Human Population (Hua Tang and Kun Tang)Data Integration Methods in Genome Wide Association Studies (Ning Sun and Hongyu Zhao)Causal Inference (Zhi Geng)General Methods:Survival Analysis (D Y Lin)Nonparametric Regression Models for the Analysis of Longitudinal Data (Colin O Wu, Xin Tian, Kai F Yu, and Mi-Xia Wu)Local Modeling: Density Estimation and Nonparametric Regression (Jianqing Fan and Runze Li)Statistical Methods for Dependent Data (Feng Chen)Bayesian Methods (Ming-Hui Chen and Keying Ye)Valid Prior-Free Probabilistic Inference and Its Applications in Medical Statistics (Duncan Ermini Leaf, Hyokun Yun, and Chuanhai Liu)Stochastic Processes and Their Applications in Medical Science (Caixia Li and Jiqian Fang)Interpolation of Missing Values and Adjustment of Moving Holiday Effect in Time Series (Zhang Jin-Xin, Zhang Xi, Xue Yun-Lian, Li Ji-Bin and Huang Bo)Tree-based Methods (Heping Zhang)Introduction to Artificial Neural Networks (Xia Jielai, Jiang Hongwei, and Tang Qiyi) Readership: Biostatisticians, applied statisticians, medical researchers and clinicians, biopharmaceutical researchers, public health epidemiologists, biometricians and applied mathematicians. Key Features: The book covers very broad topics in medical statistics The book covers both most recent developments as well as classical work of the selected areas The book chapter is written by the experts in the field and illustrated with real life examplesKeywords:Medicine;Statistics;Epidemiology;Genomics;Clinical Trials;Bioinformatics;Machine Learning;Statistical Theory;Public HealthReviews: Review of the First Edition: “Overall the book covers a wide variety of applications. Each method is presented in sufficient depth to allow the reader to understand when the method(s) can be used … this book would be a useful resource for any practitioner in medical research.” Statistical Methods in Medical Research
Systematic treatment of the commonly employed crossed and nested classification models used in analysis of variance designs with a detailed and thorough discussion of certain random effects models not commonly found in texts at the introductory or intermediate level. It also includes numerical examples to analyze data from a wide variety of disciplines as well as any worked examples containing computer outputs from standard software packages such as SAS, SPSS, and BMDP for each numerical example.
Volume 2 Biostatistical Analysis of Clinical Trials
Author: Karl E. Peace,Ding-Geng Chen,Sandeep Menon
This BASS book Series publishes selected high-quality papers reflecting recent advances in the design and biostatistical analysis of biopharmaceutical experiments – particularly biopharmaceutical clinical trials. The papers were selected from invited presentations at the Biopharmaceutical Applied Statistics Symposium (BASS), which was founded by the first Editor in 1994 and has since become the premier international conference in biopharmaceutical statistics. The primary aims of the BASS are: 1) to raise funding to support graduate students in biostatistics programs, and 2) to provide an opportunity for professionals engaged in pharmaceutical drug research and development to share insights into solving the problems they encounter. The BASS book series is initially divided into three volumes addressing: 1) Design of Clinical Trials; 2) Biostatistical Analysis of Clinical Trials; and 3) Pharmaceutical Applications. This book is the second of the 3-volume book series. The topics covered include: Statistical Approaches to the Meta-analysis of Randomized Clinical Trials, Collaborative Targeted Maximum Likelihood Estimation to Assess Causal Effects in Observational Studies, Generalized Tests in Clinical Trials, Discrete Time-to-event and Score-based Methods with Application to Composite Endpoint for Assessing Evidence of Disease Activity-Free , Imputing Missing Data Using a Surrogate Biomarker: Analyzing the Incidence of Endometrial Hyperplasia, Selected Statistical Issues in Patient-reported Outcomes, Network Meta-analysis, Detecting Safety Signals Among Adverse Events in Clinical Trials, Applied Meta-analysis Using R, Treatment of Missing Data in Comparative Effectiveness Research, Causal Estimands: A Common Language for Missing Data, Bayesian Subgroup Analysis with Examples, Statistical Methods in Diagnostic Devices, A Question-Based Approach to the Analysis of Safety Data, Analysis of Two-stage Adaptive Seamless Trial Design, and Multiplicity Problems in Clinical Trials – A Regulatory Perspective.
Praise for the Second Edition: “...a grand feast for biostatisticians. It stands readyto satisfy the appetite of any pharmaceutical scientist with arespectable statistical appetite.” —Journal of ClinicalResearch Best Practices The Third Edition of Design and Analysis of ClinicalTrials provides complete, comprehensive, and expanded coverageof recent health treatments and interventions. Featuring a unifiedpresentation, the book provides a well-balanced summary of currentregulatory requirements and recently developed statistical methodsas well as an overview of the various designs and analyses that areutilized at different stages of clinical research and development.Additional features of this Third Edition include: • New chapters on biomarker development and targetclinical trials, adaptive design, trials for evaluating diagnosticdevices, statistical methods for translational medicine, andtraditional Chinese medicine • A balanced overview of current and emerging clinicalissues as well as newly developed statistical methodologies • Practical examples of clinical trials that demonstrateeveryday applicability, with illustrations and examples to explainkey concepts • New sections on bridging studies and global trials, QTstudies, multinational trials, comparative effectiveness trials,and the analysis of QT/QTc prolongation • A complete and balanced presentation of clinical andscientific issues, statistical concepts, and methodologies forbridging clinical and statistical disciplines • An update of each chapter that reflects changes inregulatory requirements for the drug review and approval processand recent developments in statistical design and methodology forclinical research and development Design and Analysis of Clinical Trials, Third Editioncontinues to be an ideal clinical research reference for academic,pharmaceutical, medical, and regulatory scientists/researchers,statisticians, and graduate-level students.
A practical guide to methods of survival analysis for medical researchers with limited statistical experience. Methods and techniques described range from descriptive and exploratory analysis to multivariate regression methods. Uses illustrative data from actual clinical trials and observational studies to describe methods of analysing and reporting results. Also reviews the features and performance of statistical software available for applying the methods of analysis discussed.
This IMA Volume in Mathematics and its Applications STATISTICAL MODELS IN EPIDEMIOLOGY, THE ENVIRONMENT,AND CLINICAL TRIALS is a combined proceedings on "Design and Analysis of Clinical Trials" and "Statistics and Epidemiology: Environment and Health. " This volume is the third series based on the proceedings of a very successful 1997 IMA Summer Program on "Statistics in the Health Sciences. " I would like to thank the organizers: M. Elizabeth Halloran of Emory University (Biostatistics) and Donald A. Berry of Duke University (Insti tute of Statistics and Decision Sciences and Cancer Center Biostatistics) for their excellent work as organizers of the meeting and for editing the proceedings. I am grateful to Seymour Geisser of University of Minnesota (Statistics), Patricia Grambsch, University of Minnesota (Biostatistics); Joel Greenhouse, Carnegie Mellon University (Statistics); Nicholas Lange, Harvard Medical School (Brain Imaging Center, McLean Hospital); Barry Margolin, University of North Carolina-Chapel Hill (Biostatistics); Sandy Weisberg, University of Minnesota (Statistics); Scott Zeger, Johns Hop kins University (Biostatistics); and Marvin Zelen, Harvard School of Public Health (Biostatistics) for organizing the six weeks summer program. I also take this opportunity to thank the National Science Foundation (NSF) and the Army Research Office (ARO), whose financial support made the workshop possible. Willard Miller, Jr.
To reach reasoned decisions involving issues of public policy and law, statistical data and studies often need to be assessed for their accuracy and relevance. This two-volume set presents a unique and comprehensive treatment of statistical methods in legal practice. Designed to serve as a text or reference, the book presents basic concepts of probability and statistical inference applied to actual data arising from court cases concerning discrimination, trademark evidence, environmental and occupational exposure to toxic chemicals, and related health and safety topics. Substantial attention is devoted to assessing the strengths and weaknesses of statistical studies, with examples illustrating why some health studies may not have been properly designed at the outset and how actual decisions might have been reversed had more appropriate analysis of data been available to the court. This book will be of interest to lawyers and other practitioners of the law, as well as to students and researchers in the areas of statistics, statistical economics, political science, and law.
A new edition of the comprehensive, hands-on guide to financialtime series, now featuring S-Plus® and R software Time Series: Applications to Finance with R and S-Plus®,Second Edition is designed to present an in-depth introduction tothe conceptual underpinnings and modern ideas of time seriesanalysis. Utilizing interesting, real-world applications and thelatest software packages, this book successfully helps readersgrasp the technical and conceptual manner of the topic in order togain a deeper understanding of the ever-changing dynamics of thefinancial world. With balanced coverage of both theory and applications, thisSecond Edition includes new content to accurately reflect thecurrent state-of-the-art nature of financial time series analysis.A new chapter on Markov Chain Monte Carlo presents Bayesian methodsfor time series with coverage of Metropolis-Hastings algorithm,Gibbs sampling, and a case study that explores the relevance ofthese techniques for understanding activity in the Dow JonesIndustrial Average. The author also supplies a new presentation ofstatistical arbitrage that includes discussion of pairs trading andcointegration. In addition to standard topics such as forecastingand spectral analysis, real-world financial examples are used toillustrate recent developments in nonstandard techniques,including: Nonstationarity Heteroscedasticity Multivariate time series State space modeling and stochastic volatility Multivariate GARCH Cointegration and common trends The book's succinct and focused organization allows readers tograsp the important ideas of time series. All examples aresystematically illustrated with S-Plus® and R software,highlighting the relevance of time series in financialapplications. End-of-chapter exercises and selected solutions allowreaders to test their comprehension of the presented material, anda related Web site features additional data sets. Time Series: Applications to Finance with R and S-Plus® isan excellent book for courses on financial time series at theupper-undergraduate and beginning graduate levels. It also servesas an indispensible resource for practitioners working withfinancial data in the fields of statistics, economics, business,and risk management.
Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.
A comprehensive, step-by-step introduction to wavelets in statistics. What are wavelets? What makes them increasingly indispensable in statistical nonparametrics? Why are they suitable for "time-scale" applications? How are they used to solve such problems as denoising, regression, or density estimation? Where can one find up-to-date information on these newly "discovered" mathematical objects? These are some of the questions Brani Vidakovic answers in Statistical Modeling by Wavelets. Providing a much-needed introduction to the latest tools afforded statisticians by wavelet theory, Vidakovic compiles, organizes, and explains in depth research data previously available only in disparate journal articles. He carefully balances both statistical and mathematical techniques, supplementing the material with a wealth of examples, more than 100 illustrations, and extensive references-with data sets and S-Plus wavelet overviews made available for downloading over the Internet. Both introductory and data-oriented modeling topics are featured, including: * Continuous and discrete wavelet transformations. * Statistical optimality properties of wavelet shrinkage. * Theoretical aspects of wavelet density estimation. * Bayesian modeling in the wavelet domain. * Properties of wavelet-based random functions and densities. * Several novel and important wavelet applications in statistics. * Wavelet methods in time series. Accessible to anyone with a background in advanced calculus and algebra, Statistical Modeling by Wavelets promises to become the standard reference for statisticians and engineers seeking a comprehensive introduction to an emerging field.
Over the last twenty years there has been a dramatic upsurge in the application of meta-analysis to medical research. This has mainly been due to greater emphasis on evidence-based medicine and the need for reliable summaries of the vast and expanding volume of clinical research. At the same time there have been great strides in the development and refinement of the associated statistical methodology. This book describes the planning, conduct and reporting of a meta-analysis as applied to a series of randomized controlled clinical trials. * The various approaches are presented within a general unified framework. * Meta-analysis techniques are described in detail, from their theoretical development through to practical implementation. * Each topic discussed is supported by detailed worked examples. * A comparison of fixed and random effects approaches is included, as well as a discussion of Bayesian methods and cumulative meta-analysis. * Fully documented programs using standard statistical procedures in SAS are available on the Web. Ideally suited for practising statisticians and statistically-minded medical professionals, the book will also be of use to graduate students of medical statistics. The book is a self-contained and comprehensive account of the subject and an essential purchase for anyone involved in clinical trials.
Identifying Influential Data and Sources of Collinearity
Author: David A. Belsley,Edwin Kuh,Roy E. Welsch
Publisher: John Wiley & Sons
The Wiley-Interscience Paperback Series consists of selected booksthat have been made more accessible to consumers in an effort toincrease global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists. "The title of the book more or less sums up the contents. Itappears to me to represent a real breakthrough in the art ofdealing in ‘unconventional’ data. . . . I found thewhole book both readable and enjoyable. It is suitable for dataanalysts, academic statisticians, and professional softwarewriters." –Journal of the Royal Statistical Society "The book assumes a working knowledge of all of the principalresults and techniques used in least squares multiple regression,as expressed in vector and matrix notation. Given this background,the book is clear and easy to use. . . . The techniques areillustrated in great detail with practical data sets fromeconometrics." –Short Book Reviews, International Statistical Institute Regression Diagnostics: Identifying Influential Data and Sourcesof Collinearity provides practicing statisticians andeconometricians with new tools for assessing quality andreliability of regression estimates. Diagnostic techniques aredeveloped that aid in the systematic location of data points thatare unusual or inordinately influential; measure the presence andintensity of collinear relations among the regression data; andhelp to identify variables involved in each and pinpoint estimatedcoefficients potentially most adversely affected. The bookemphasizes diagnostics and includes suggestions for remedialaction
Author: Jay Bartroff,Tze Leung Lai,Mei-Chiung Shih
Publisher: Springer Science & Business Media
Sequential Experimentation in Clinical Trials: Design and Analysis is developed from decades of work in research groups, statistical pedagogy, and workshop participation. Different parts of the book can be used for short courses on clinical trials, translational medical research, and sequential experimentation. The authors have successfully used the book to teach innovative clinical trial designs and statistical methods for Statistics Ph.D. students at Stanford University. There are additional online supplements for the book that include chapter-specific exercises and information. Sequential Experimentation in Clinical Trials: Design and Analysis covers the much broader subject of sequential experimentation that includes group sequential and adaptive designs of Phase II and III clinical trials, which have attracted much attention in the past three decades. In particular, the broad scope of design and analysis problems in sequential experimentation clearly requires a wide range of statistical methods and models from nonlinear regression analysis, experimental design, dynamic programming, survival analysis, resampling, and likelihood and Bayesian inference. The background material in these building blocks is summarized in Chapter 2 and Chapter 3 and certain sections in Chapter 6 and Chapter 7. Besides group sequential tests and adaptive designs, the book also introduces sequential change-point detection methods in Chapter 5 in connection with pharmacovigilance and public health surveillance. Together with dynamic programming and approximate dynamic programming in Chapter 3, the book therefore covers all basic topics for a graduate course in sequential analysis designs.
Epidemiologic studies provide research strategies for investigating public health and scientific questions relating to the factors that cause and prevent ailments in human populations. Statistics in Epidemiology: Methods, Techniques and Applications presents a comprehensive review of the wide range of principles, methods and techniques underlying prospective, retrospective and cross-sectional approaches to epidemiologic studies. Written for epidemiologists and other researchers without extensive backgrounds in statistics, this new book provides a clear and concise description of the statistical tools used in epidemiology. Emphasis is given to the application of these statistical tools, and examples are provided to illustrate direct methods for applying common statistical techniques in order to obtain solutions to problems. Statistics in Epidemiology: Methods, Techniques and Applications goes beyond the elementary material found in basic epidemiology and biostatistics books and provides a detailed account of techniques:
A indispensable guide to understanding and designing modern experiments The tools and techniques of Design of Experiments (DOE) allow researchers to successfully collect, analyze, and interpret data across a wide array of disciplines. Statistical Analysis of Designed Experiments provides a modern and balanced treatment of DOE methodology with thorough coverage of the underlying theory and standard designs of experiments, guiding the reader through applications to research in various fields such as engineering, medicine, business, and the social sciences. The book supplies a foundation for the subject, beginning with basic concepts of DOE and a review of elementary normal theory statistical methods. Subsequent chapters present a uniform, model-based approach to DOE. Each design is presented in a comprehensive format and is accompanied by a motivating example, discussion of the applicability of the design, and a model for its analysis using statistical methods such as graphical plots, analysis of variance (ANOVA), confidence intervals, and hypothesis tests. Numerous theoretical and applied exercises are provided in each chapter, and answers to selected exercises are included at the end of the book. An appendix features three case studies that illustrate the challenges often encountered in real-world experiments, such as randomization, unbalanced data, and outliers. Minitab® software is used to perform analyses throughout the book, and an accompanying FTP site houses additional exercises and data sets. With its breadth of real-world examples and accessible treatment of both theory and applications, Statistical Analysis of Designed Experiments is a valuable book for experimental design courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for practicing statisticians, engineers, and scientists who would like to further their knowledge of DOE.