This contributed volume convenes selected, peer-reviewed works presented at the BIOMAT 2021 International Symposium, which was virtually held on November 1-5, 2021, with its organization staff based in Rio de Janeiro, Brazil. In this volume the reader will find applications of mathematical modeling on health, ecology, and social interactions, addressing topics like probability distributions of mutations in different cancer cell types; oscillations in biological systems; modeling of marine ecosystems; mathematical modeling of organs and tissues at the cellular level; as well as studies on novel challenges related to COVID-19, including the mathematical analysis of a pandemic model targeting effective vaccination strategy and the modeling of the role of media coverage on mitigating the spread of infectious diseases. Held every year since 2001, the BIOMAT International Symposium gathers together, in a single conference, researchers from Mathematics, Physics, Biology, and affine fields to promote the interdisciplinary exchange of results, ideas and techniques, promoting truly international cooperation for problem discussion. BIOMAT volumes published from 2017 to 2020 are also available by Springer.

Handbook of Spatial Epidemiology explains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists, geographers, and statisticians share interdisciplinary viewpoints on analyzing spatial data and space-time variations in disease incidences. These analyses can provide important information that leads to better decision making in public health. The first part of the book addresses general issues related to epidemiology, GIS, environmental studies, clustering, and ecological analysis. The second part presents basic statistical methods used in spatial epidemiology, including fundamental likelihood principles, Bayesian methods, and testing and nonparametric approaches. With a focus on special methods, the third part describes geostatistical models, splines, quantile regression, focused clustering, mixtures, multivariate methods, and much more. The final part examines special problems and application areas, such as residential history analysis, segregation, health services research, health surveys, infectious disease, veterinary topics, and health surveillance and clustering. Spatial epidemiology, also known as disease mapping, studies the geographical or spatial distribution of health outcomes. This handbook offers a wide-ranging overview of state-of-the-art approaches to determine the relationships between health and various risk factors, empowering researchers and policy makers to tackle public health problems.

Accurate sample size calculation ensures that clinical studies have adequate power to detect clinically meaningful effects. This results in the efficient use of resources and avoids exposing a disproportionate number of patients to experimental treatments caused by an overpowered study. Sample Size Calculations for Clustered and Longitudinal Outcomes in Clinical Research explains how to determine sample size for studies with correlated outcomes, which are widely implemented in medical, epidemiological, and behavioral studies. The book focuses on issues specific to the two types of correlated outcomes: longitudinal and clustered. For clustered studies, the authors provide sample size formulas that accommodate variable cluster sizes and within-cluster correlation. For longitudinal studies, they present sample size formulas to account for within-subject correlation among repeated measurements and various missing data patterns. For multiple levels of clustering, the level at which to perform randomization actually becomes a design parameter. The authors show how this can greatly impact trial administration, analysis, and sample size requirement. Addressing the overarching theme of sample size determination for correlated outcomes, this book provides a useful resource for biostatisticians, clinical investigators, epidemiologists, and social scientists whose research involves trials with correlated outcomes. Each chapter is self-contained so readers can explore topics relevant to their research projects without having to refer to other chapters.

In clinical trial practice, controversial statistical issues inevitably occur regardless of the compliance with good statistical practice and good clinical practice. But by identifying the causes of the issues and correcting them, the study objectives of clinical trials can be better achieved. Controversial Statistical Issues in Clinical Trials covers commonly encountered controversial statistical issues in clinical trials and, whenever possible, makes recommendations to resolve these problems. The book focuses on issues occurring at various stages of clinical research and development, including early-phase clinical development (such as bioavailability/bioequivalence), bench-to-bedside translational research, and late-phase clinical development. Numerous examples illustrate the impact of these issues on the evaluation of the safety and efficacy of the test treatment under investigation. The author also offers recommendations regarding possible resolutions of the problems. Written by one of the preeminent experts in the field, this book provides a useful desk reference and state-of-the art examination of problematic issues in clinical trials for scientists in the pharmaceutical industry, medical/statistical reviewers in government regulatory agencies, and researchers and students in academia.

Theory of illness causation is an important issue in all biomedical sciences, and solid etiological explanations are needed in order to develop therapeutic approaches in medicine and preventive interventions in public health. Until now, the literature about the theoretical underpinnings of illness causation research has been scarce and fragmented, and lacking a convenient summary. This interdisciplinary book provides a convenient and accessible distillation of the current status of research into this developing field, and adds a personal flavor to the discussion by proposing the etiological stance as a comprehensive approach to identify modifiable causes of illness. Key Features * Provides a synthesis of the epidemiological and philosophical concepts in this growing research area * Gives an accessible overview of current methods in biomedical causal metaphysics what is a cause of illness? and epistemology how do we identify it? * Proposes a novel approach that integrates modern epidemiological methodology and recent theories from philosophy of science Written for postgraduate students and researchers in the health and biomedical sciences, including those undertaking courses in the philosophy of medicine/science, public and global health, introduction to epidemiology, research methods, and advanced reasoning, the content will also be of interest to practicing public health workers, biomedical scientists, and physicians. ABOUT THE AUTHOR Olaf Dammann is Professor and Vice Chair of Public Health and Community Medicine at Tufts University School of Medicine, Boston, Massachusetts, USA; as well as a Professor in the Department of Gynecology and Obstetrics at Hannover Medical School, Hannover, Germany. Cover image: Mask used by "Eskimo" shaman in causation of illness. Credit: Wellcome Collection. CC BY https://creativecommons.org/licenses/by/4.0

The average age of the world's population is increasing at an unprecedented rate and this increase is changing the world. This "Silver tsunami" emphasizes the need to provide advanced training in epidemiology and increase the cadre of experts in the study of aging. This book is designed to summarize unique methodological issues relevant to the study of aging, biomarkers of aging and the biology/physiology of aging and in-depth discussions of the etiology and epidemiology of common geriatric syndromes and diseases. Contributing authors in the book represent many disciplines, not only epidemiology and clinical geriatrics, but also demography, health services, research, cardiovascular disease, diabetes, psychiatry, neurology, social services, musculoskeletal diseases and cancer. The aim of the book is to provide a broad multidisciplinary background for any student/researcher interested in aging. The material in the book is organized and comprehensive. It represents the most up-to-date information on the scientific issues in aging research written by academics who specialize in research and training in the broad field of aging. The structure and organization of the book reflects our course series in the Epidemiology of Aging starting with the broad issues of demography and methodology, and then addressing specific health conditions and geriatric conditions common to older persons.

This book aims to clarify the potential association between frailty and cardiovascular disease in older people. Covering the biological as well as the clinical point of view, it allows researchers and clinicians to discover the significance of this topic. The contributions cover the most important aspects in the potential relationship between frailty and cardiovascular disease. In particular, authoritative authors in this field have clarified the definition and the epidemiology of frailty and cardiovascular disease in older people. A large part of the volume is dedicated to the biological mechanisms of frailty and cardiovascular disease, trying to find those in common between these two conditions. Since this book is dedicated to both researchers and clinicians, we have proposed some chapters to the importance of comprehensive geriatric assessment in the evaluation and treatment of cardiovascular diseases and frailty. In this regard, the importance of geriatric evaluation in cardiac surgery for older people is well covered. Finally, the importance of cardiac rehabilitation and physical exercise is summarized, being, actually, the most important treatments for both frailty and cardiovascular disease. Written by many well-known and widely published experts in their respective fields, this book will appeal to a wide readership such as researchers in the field and clinicians, especially suited in geriatric medicine and cardiology who, every day, face frail older patients.

Comparing and Contrasting the Impact of the COVID-19 Pandemic in the European Union challenges the use of uncontextualised comparisons of COVID-19 cases and deaths in member states during the period when Europe was the epicentre of the pandemic. This timely study looks behind the headlines and the statistics to demonstrate the value for knowledge exchange and policy learning of comparisons that are founded on an in-depth understanding of key socio-demographic and public health indicators within their policy settings. The book adopts innovative, integrated, multi-disciplinary international perspectives to track and assess a fast-moving topical subject in an accessible format. It offers a template for analysing policy responses to the COVID-19 pandemic and for using evidence-based comparisons to inform and support policy development.

Epidemic trend analysis, timeline progression, prediction, and recommendation are critical for initiating effective public health control strategies, and AI and data analytics play an important role in epidemiology, diagnostic, and clinical fronts. The focus of this book is data analytics for COVID-19, which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussions on data models, their performance, different big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies. Aimed at Data Analysts, Epidemiologists and associated researchers, this book: discusses challenges of AI model for big data analytics in pandemic scenarios; explains how different big data analytics techniques can be implemented; provides a set of recommendations to minimize infection rate of COVID-19; summarizes various techniques of data processing and knowledge extraction; enables users to understand big data analytics techniques required for prediction purposes.

Epidemiology is often referred to as the science of public health. However, unlike other major sciences, its theoretical foundations are rarely articulated. While the idea of epidemiologic theory may seem dry and arcane, it is at its core about explaining the people's health. It is about life and death. It is about biology and society. It is about ecology and the economy. It is about how myriad aspects of people's lives - involving work, dignity, desire, love, play, conflict, discrimination, and injustice - become literally incorporated into our bodies and manifest in our health status, individually and collectively. And it is about essential knowledge critical for improving the people's health and minimizing inequitable burdens of disease, disability, and death. Woven from a vast array of schools of thought, including those in the natural, social, and biomedical sciences, epidemiologic theory is a rich tapestry whose time for analysis is long overdue. By tracing its history and contours from ancient societies on through the development of - and debates within - contemporary epidemiology worldwide, Dr. Krieger shows how epidemiologic theory has long shaped epidemiologic practice, knowledge, and the politics of public health. Outlining an ecosocial theory of disease distribution that situates both population health and epidemiologic theory in societal and ecologic context, she offers a more holistic picture of how we embody the human experience. This concise, conceptually rich, and accessible book is a rallying cry for a return to the study and discussion of epidemiologic theory: what it is, why it matters, how it has changed over time, and its implications for improving population health and promoting health equity. It should be required reading for all epidemiologists, or anyone involved in the study of human health and well-being.

Absolute Risk: Methods and Applications in Clinical Management and Public Health provides theory and examples to demonstrate the importance of absolute risk in counseling patients, devising public health strategies, and clinical management. The book provides sufficient technical detail to allow statisticians, epidemiologists, and clinicians to build, test, and apply models of absolute risk. Features: Provides theoretical basis for modeling absolute risk, including competing risks and cause-specific and cumulative incidence regression Discusses various sampling designs for estimating absolute risk and criteria to evaluate models Provides details on statistical inference for the various sampling designs Discusses criteria for evaluating risk models and comparing risk models, including both general criteria and problem-specific expected losses in well-defined clinical and public health applications Describes many applications encompassing both disease prevention and prognosis, and ranging from counseling individual patients, to clinical decision making, to assessing the impact of risk-based public health strategies Discusses model updating, family-based designs, dynamic projections, and other topics Ruth M. Pfeiffer is a mathematical statistician and Fellow of the American Statistical Association, with interests in risk modeling, dimension reduction, and applications in epidemiology. She developed absolute risk models for breast cancer, colon cancer, melanoma, and second primary thyroid cancer following a childhood cancer diagnosis. Mitchell H. Gail developed the widely used "Gail model" for projecting the absolute risk of invasive breast cancer. He is a medical statistician with interests in statistical methods and applications in epidemiology and molecular medicine. He is a member of the National Academy of Medicine and former President of the American Statistical Association. Both are Senior Investigators in the Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health.

Straightforward Statistics: Understanding the Tools of Research is a clear and direct introduction to statistics for the social, behavioral, and life sciences. Based on the author's extensive experience teaching undergraduate statistics, this book provides a narrative presentation of the core principles that provide the foundation for modern-day statistics. With step-by-step guidance on the nuts and bolts of computing these statistics, the book includes detailed tutorials how to use state-of-the-art software, SPSS, to compute the basic statistics employed in modern academic and applied research. Across 13 succinct chapters, this text presents statistics using a conceptual approach along with information on the relevance of the different tools in different contexts and summaries of current research examples. Students should find this book easy useful and engaging in its presentation while instructors should find it detailed, comprehensive, accessible, and helpful in complementing a basic course in statistics.

Medical Data Management is a systematic introduction to the basic methodology of professional clinical data management. It emphasizes generic methods of medical documentation applicable to such diverse tasks as the electronic patient record, maintaining a clinical trials database, and building a tumor registry. This book is for all students in medical informatics and health information management, and it is ideal for both the undergraduate and the graduate levels. The book also guides professionals in the design and use of clinical information systems in various health care settings. It is an invaluable resource for all health care professionals involved in designing, assessing, adapting, or using clinical data management systems in hospitals, outpatient clinics, study centers, health plans, etc. The book combines a consistent theoretical foundation of medical documentation methods outlining their practical applicability in real clinical data management systems. Two new chapters detail hospital information systems and clinical trials. There is a focus on the international classification of diseases (ICD-9 and -10) systems, as well as a discussion on the difference between the two codes. All chapters feature exercises, bullet points, and a summary to provide the reader with essential points to remember. New to the Third Edition is a comprehensive section comprised of a combined Thesaurus and Glossary which aims to clarify the unclear and sometimes inconsistent terminology surrounding the topic.

Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem, social, and infrastructure resilience, given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies. This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts. The chapters unveil topics of atmospheric circulation, climate modeling, and long-term prediction; approach the problems of increasing frequency of extreme events, sea level rise, and forest fires, as well as economic losses, analysis of climate impacts for insurance, agriculture, fisheries, and electric and transport infrastructures. The reader will be exposed to the current research using a variety of methods from physical modeling, statistics, and machine learning, including the global circulation models (GCM) and ocean models, statistical generalized additive models (GAM) and generalized linear models (GLM), state space and graphical models, causality networks, Bayesian ensembles, a variety of index methods and statistical tests, and machine learning methods. The reader will learn about data from various sources, including GCM and ocean model outputs, satellite observations, and data collected by different agencies and research units. Many of the chapters provide references to open source software R and Python code that are available for implementing the methods.

A fully updated edition of this key text on mixed models, focusing on applications in medical research The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott's groundbreaking text provides an update on the latest developments, and includes guidance on the use of current SAS techniques across a wide range of applications. Presents an overview of the theory and applications of mixed models in medical research, including the latest developments and new sections on incomplete block designs and the analysis of bilateral data. Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists. Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output. Features the new version of SAS, including new graphics for model diagnostics and the procedure PROC MCMC. Supported by a website featuring computer code, data sets, and further material. This third edition will appeal to applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The book will also be of great value to a broad range of scientists, particularly those working in the medical and pharmaceutical areas.

The Global Burden of Disease Study (GBD) is one of the largest-scale research collaborations in global health, distilling a wide range of health information to provide estimates and projections for more than 350 diseases, injuries, and risk factors in 195 countries. Its results are a critical tool informing researchers, policy-makers, and others working to promote health around the globe. A study like the GBD is, of course, extremely complex from an empirical perspective. But it also raises a large number of complex ethical and philosophical questions that have been explored in a series of collaborations over the past twenty years among epidemiologists, philosophers, economists, and policy scholars. The essays in this volume address issues of current and urgent concern to the GBD and other epidemiological studies, including rival understandings of causation, the aggregation of complex health data, temporal discounting, age-weighting, and the valuation of health states. The volume concludes with a set of chapters discussing how epidemiological data should and should not be used. Better appreciating the philosophical dimensions of a study like the GBD can make possible a more sophisticated interpretation of its results, and it can improve epidemiological studies in the future, so that they are better suited to produce results that can help us to improve global health.

Theory of illness causation is an important issue in all biomedical sciences, and solid etiological explanations are needed in order to develop therapeutic approaches in medicine and preventive interventions in public health. Until now, the literature about the theoretical underpinnings of illness causation research has been scarce and fragmented, and lacking a convenient summary. This interdisciplinary book provides a convenient and accessible distillation of the current status of research into this developing field, and adds a personal flavor to the discussion by proposing the etiological stance as a comprehensive approach to identify modifiable causes of illness. Key Features * Provides a synthesis of the epidemiological and philosophical concepts in this growing research area * Gives an accessible overview of current methods in biomedical causal metaphysics what is a cause of illness? and epistemology how do we identify it? * Proposes a novel approach that integrates modern epidemiological methodology and recent theories from philosophy of science Written for postgraduate students and researchers in the health and biomedical sciences, including those undertaking courses in the philosophy of medicine/science, public and global health, introduction to epidemiology, research methods, and advanced reasoning, the content will also be of interest to practicing public health workers, biomedical scientists, and physicians. ABOUT THE AUTHOR Olaf Dammann is Professor and Vice Chair of Public Health and Community Medicine at Tufts University School of Medicine, Boston, Massachusetts, USA; as well as a Professor in the Department of Gynecology and Obstetrics at Hannover Medical School, Hannover, Germany. Cover image: Mask used by "Eskimo" shaman in causation of illness. Credit: Wellcome Collection. CC BY https://creativecommons.org/licenses/by/4.0

Recognised as the most influential publication in the field, ARM facilitates deep understanding of the Rasch model and its practical applications. The authors review the crucial properties of the model and demonstrate its use with examples across the human sciences. Readers will be able to understand and critically evaluate Rasch measurement research, perform their own Rasch analyses and interpret their results. The glossary and illustrations support that understanding, and the accessible approach means that it is ideal for readers without a mathematical background. Highlights of the new edition include: More learning tools to strengthen readers' understanding including chapter introductions, boldfaced key terms, chapter summaries, activities and suggested readings. Greater emphasis on the use of R packages; readers can download the R code from the Routledge website. Explores the distinction between numerical values, quantity and units, to understand the measurement and the role of the Rasch logit scale (Chapter 4). A new four-option data set from the IASQ (Instrumental Attitude towards Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (Chapter 6). Clarifies the relationship between Rasch measurement, path analysis and SEM, with a host of new examples of Rasch measurement applied across health sciences, education and psychology (Chapter 10). Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business, and other social and health sciences. Professionals in these areas will also appreciate the book's accessible introduction.

Applied Problem-Solving in Healthcare Management is a practical textbook devoted to developing and strengthening problem-solving and decision-making leadership competencies of healthcare administration students and healthcare management professionals. Built upon the University of Minnesota Master of Healthcare Administration Program's Problem-Solving Method, the text describes the "never assume" mindset and the structured method that drive evidence-based, action-oriented problem-solving. The "never assume" mindset requires healthcare leaders to understand themselves and their stakeholders, and to engage in waves of divergent and convergent thinking. This structured method guides the problem solver through the phases of defining, studying, and acting on complex interrelated organizational problems that involve multiple root causes. The book also describes how the Problem-Solving Method is complementary to quality improvement methods and can be used in healthcare organizations along with Lean, Design Thinking, and Human Centered Design. Providing step-by-step instruction including useful tips, tools, activities, and case studies, this effective resource demonstrates the utility of the method for all types of health organization settings including health systems, hospitals, clinics, population health, and long-term care. For students taking health management, capstone, and experiential learning courses, including internship and residency projects, this book allows them to test and apply their problem-solving and decision-making skills to real-world situations. Beyond the classroom, it is an indispensable resource for organizations seeking to enhance the problem-solving skills of their workforce. The authors of the text have nearly 75 years of combined experience in healthcare management, leadership, and professional consulting, and teaching and advising healthcare administration students in classrooms, on student capstone, internship and residency projects, and case competitions. Synthesizing their expertise, this text serves as a guide for those who wish to strengthen their problem-solving abilities to systematically identify, analyze, study, and solve pressing organizational challenges in healthcare settings. Key Features: Describes a mindset and a structured problem-solving method that builds leadership competencies Encourages a step-by-step problem-solving approach to define, study, and act on problems to drive action-oriented solutions Supports experiential learning and coaching for students and professionals early in their careers, applicable especially to healthcare management, capstone, and student consulting courses, internship and residency projects, case competitions, and professional development in organizations Compares the Problem-Solving Method to other complementary methods used in many healthcare organizations, including Lean, Design Thinking, and Human Centered Design Includes access to the fully downloadable eBook as well as ancillary materials such as Instructor's Manual and Sample Syllabi

As the novel coronavirus (Covid-19) spread around the world, so did theories, stories, and conspiracy beliefs about it. These theories infected communities from the halls of Congress to Facebook groups, spreading quickly in newspapers, on various social media and between friends. They spurred debate about the origins, treatment options and responses to the virus, creating distrust towards public health workers and suspicion of vaccines. This book examines the most popular Covid-19 theories, connecting current conspiracy beliefs to long-standing fears and urban legends. By examining the vehicles and mechanisms of Covid-19 conspiracy, readers can better understand how theories spread and how to respond to misinformation.

Bayesian Demographic Estimation and Forecasting presents three statistical frameworks for modern demographic estimation and forecasting. The frameworks draw on recent advances in statistical methodology to provide new tools for tackling challenges such as disaggregation, measurement error, missing data, and combining multiple data sources. The methods apply to single demographic series, or to entire demographic systems. The methods unify estimation and forecasting, and yield detailed measures of uncertainty. The book assumes minimal knowledge of statistics, and no previous knowledge of demography. The authors have developed a set of R packages implementing the methods. Data and code for all applications in the book are available on www.bdef-book.com. "This book will be welcome for the scientific community of forecasters...as it presents a new approach which has already given important results and which, in my opinion, will increase its importance in the future." ~Daniel Courgeau, Institut national d'etudes demographiques

Building on its best-selling predecessors, Basic Statistics and Pharmaceutical Statistical Applications, Third Edition covers statistical topics most relevant to those in the pharmaceutical industry and pharmacy practice. It focuses on the fundamentals required to understand descriptive and inferential statistics for problem solving. Incorporating new material in virtually every chapter, this third edition now provides information on software applications to assist with evaluating data. New to the Third Edition Use of Excel (R) and Minitab (R) for performing statistical analysis Discussions of nonprobability sampling procedures, determining if data is normally distributed, evaluation of covariances, and testing for precision equivalence Expanded sections on regression analysis, chi square tests, tests for trends with ordinal data, and tests related to survival statistics Additional nonparametric procedures, including the one-sided sign test, Wilcoxon signed-ranks test, and Mood's median test With the help of flow charts and tables, the author dispels some of the anxiety associated with using basic statistical tests in the pharmacy profession and helps readers correctly interpret their results using statistical software. Through the text's worked-out examples, readers better understand how the mathematics works, the logic behind many of the equations, and the tests' outcomes.

Disease Surveillance: Technological Contributions to Global Health Security reminds us of the continued vulnerability of the world to contagious infections. The book presents examples of disease surveillance systems and evaluates promising advances as well as opportunities for new systems. It also explains how newer technologies can allow countries to comply with the International Health Regulations established by the World Health Organization. The book covers various topics including international health regulations policy, challenges surrounding system deployment and implementation, data visualization techniques, the strengths and weaknesses of open source software, and legal considerations surrounding such software. This book will show you how new reporting requirements, combined with new technologies, big data sources, and sophisticated analytic approaches now enable the public health community to identify potential outbreaks and initiate a response earlier than at any other time in history.

This work explains the purpose of statistical methods in medical studies and analyzes the statistical techniques used by clinical investigators, with special emphasis on studies published in "The New England Journal of Medicine". It clarifies fundamental concepts of statistical design and analysis, and facilitates the understanding of research results.

The number of innovative applications of randomization tests in various fields and recent developments in experimental design, significance testing, computing facilities, and randomization test algorithms have necessitated a new edition of Randomization Tests. Updated, reorganized, and revised, the text emphasizes the irrelevance and implausibility of the random sampling assumption for the typical experiment in three completely rewritten chapters. It also discusses factorial designs and interactions and combines repeated-measures and randomized block designs in one chapter. The authors focus more attention on the practicality of N-of-1 randomization tests and the availability of user-friendly software to perform them. In addition, they provide an overview of free and commercial computer programs for all of the tests presented in the book. Building on the previous editions that have served as standard textbooks for more than twenty-five years, Randomization Tests, Fourth Edition includes downloadable resources of up-to-date randomization test programs that facilitate application of the tests to experimental data. This CD-ROM enables students to work out problems that have been added to the chapters and helps professors teach the basics of randomization tests and devise tasks for assignments and examinations.