This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equi-correlation models. This new dynamic modelling approach is utilized to develop theoretically sound inference techniques such as the generalized quasi-likelihood (GQL) technique for consistent and efficient estimation of the underlying regression effects involved in the model, whereas the existing 'working' correlations based GEE (generalized
estimating equations) approach has serious theoretical limitations both for consistent and efficient estimation, and the existing random effects based correlations approach is not suitable to model the longitudinal correlations. The book has exploited the random effects carefully only to model the correlations of the familial data. Subsequently, this book has modelled the correlations of the longitudinal data collected from the members of a large number of independent families by using the class of auto-correlation structures conditional on the random effects. The book also provides models and inferences for discrete longitudinal data in the adaptive clinical trial set up.
The book is mathematically rigorous and provides details for the development of estimation approaches under selected familial and longitudinal models. Further, while the book provides special cares for mathematics behind the correlation models, it also presents the
illustrations of the statistical analysis of various real life data.
This book will be of interest to the researchers including graduate students in biostatistics and econometrics, among other applied statistics research areas. Brajendra Sutradhar is a University Research Professor at Memorial University in St. John's, Canada. He is an elected member of the International Statistical Institute and a fellow of the American Statistical Association. He has published about 110 papers in statistics journals in the area of multivariate analysis, time series analysis including forecasting, sampling, survival analysis for correlated failure times, robust inferences in generalized linear mixed models with outliers, and generalized linear longitudinal mixed models with bio-statistical and econometric applications. He has served as an associate editor for six years for Canadian Journal of Statistics and for four years for the Journal of Environmental and Ecological Statistics. He has served for 3 years as a member of the advisory committee on statistical methods in Statistics Canada. Professor Sutradhar was awarded 2007 distinguished service award of Statistics Society of Canada for his many years of services to the
society including his special services for society's annual meetings.

Key Issues in Epidemiologic Research: An Overview.

OBJECTIVES AND METHODS OR EPIDEMIOLOGIC RESEARCH.

Fundamentals of Epidemiologic Research.

Types of Epidemiologic Research.

Design Options in Observational Studies.

Typology of Observational Study Designs.

Measures of Disease Frequency: Incidence.

Other Measures of Disease Frequency.

Measures of Association.

Measures of Potential Impact and Summary of the Measures.

VALIDITY OF EPIDEMIOLOGIC RESEARCH.

Validity: General Considerations.

Selection Bias.

Information Bias.

Confounding.

Confounding Involving Several Risk Factors.

PRINCIPLES AND PROCEDURES OF EPIDEMIOLOGIC ANALYSIS.

Statistical Inferences About Effect Measures: Simple Analysis.

Overview of Options for Control of Extraneous Factors.

Stratified Analysis.

Matching in Epidemiologic Studies.

Interaction, Effect Modification, and Synergism.

Modeling: Theoretical Considerations.

Modeling: Analysis Strategy.

Applications of Modeling with No Interaction.

Applications of Logistic Regression with Interaction, Using Unconditional ML Estimation.

Applications of Modeling: Conditional Likelihood Estimation.

Appendices.

Index.

This is the first comprehensive text on the design and analysis of group-randomized trials. It It collects information previously scattered among journals and texts in a variety of disciplines, and, in addition, presents much new material not available elsewhere. The book has been written to help those involved in these trials improve their ability to plan, fund, conduct, analyse, and interpret them, and to give students a detailed understanding of the field. Group-randomized trials are comparative studies in which the units of assignment are identifiable groups and the units of observation are members of those groups. The positive intraclass correlation expected among the members of each group poses unique and challenging issues for the design and analysis of these trials and separates them from the traditional clinical trial. After reviewing the underlying issues, Murray presents the research designs that are most widely used in group-randomized trials, together with their strengths, weaknesses, and appropriate applications. He describes the many approaches to analysis that are now available, presents mixed-model regression analyses appropriate to each design, and illustrates them using data from the Minnesota Heart Health Program. He also covers methods for estimating sample size, detectable difference, and power. This volume is not limited only to a conceptual treatment of the issues and solutions. It offers a review of the practical applications in a series of case studies, examples, and problems.

Explore Important Tools for High-Quality Work in Pharmaceutical Safety Statistical Methods for Drug Safety presents a wide variety of statistical approaches for analyzing pharmacoepidemiologic data. It covers both commonly used techniques, such as proportional reporting ratios for the analysis of spontaneous adverse event reports, and newer approaches, such as the use of marginal structural models for controlling dynamic selection bias in the analysis of large-scale longitudinal observational data. Choose the Right Statistical Approach for Analyzing Your Drug Safety Data The book describes linear and non-linear mixed-effects models, discrete-time survival models, and new approaches to the meta-analysis of rare binary adverse events. It explores research involving the re-analysis of complete longitudinal patient records from randomized clinical trials. The book discusses causal inference models, including propensity score matching, marginal structural models, and differential effects, as well as mixed-effects Poisson regression models for analyzing ecological data, such as county-level adverse event rates. The authors also cover numerous other methods useful for the analysis of within-subject and between-subject variation in adverse events abstracted from large-scale medical claims databases, electronic health records, and additional observational data streams. Advance Statistical Practice in Pharmacoepidemiology Authored by two professors at the forefront of developing new statistical methodologies to address pharmacoepidemiologic problems, this book provides a cohesive compendium of statistical methods that pharmacoepidemiologists can readily use in their work. It also encourages statistical scientists to develop new methods that go beyond the foundation covered in the text.

Discover the Latest Statistical Approaches for Modeling Exposure-Response Relationships Written by an applied statistician with extensive practical experience in drug development, Exposure-Response Modeling: Methods and Practical Implementation explores a wide range of topics in exposure-response modeling, from traditional pharmacokinetic-pharmacodynamic (PKPD) modeling to other areas in drug development and beyond. It incorporates numerous examples and software programs for implementing novel methods. The book describes using measurement error models to treat sequential modeling, fitting models with exposure and response driven by complex dynamics, and survival analysis with dynamic exposure history. It also covers Bayesian analysis and model-based Bayesian decision analysis, causal inference to eliminate confounding biases, and exposure-response modeling with response-dependent dose/treatment adjustments (dynamic treatment regimes) for personalized medicine and treatment adaptation. Many examples illustrate the use of exposure-response modeling in experimental toxicology, clinical pharmacology, epidemiology, and drug safety. Some examples demonstrate how to solve practical problems while others help with understanding concepts and evaluating the performance of new methods. The provided SAS and R codes enable readers to test the approaches in their own scenarios. Although application oriented, this book also gives a systematic treatment of concepts and methodology. Applied statisticians and modelers can find details on how to implement new approaches. Researchers can find topics for or applications of their work. In addition, students can see how complicated methodology and models are applied to practical situations.

In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author. All the R code used in the book is available at: http://jmr.r-forge.r-project.org/

Add the Empirical Likelihood to Your Nonparametric Toolbox Empirical Likelihood Method in Survival Analysis explains how to use the empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out examples with the associated R code. The datasets and code are available for download on his website and CRAN. The book focuses on all the standard survival analysis topics treated with empirical likelihood, including hazard functions, cumulative distribution functions, analysis of the Cox model, and computation of empirical likelihood for censored data. It also covers semi-parametric accelerated failure time models, the optimality of confidence regions derived from empirical likelihood or plug-in empirical likelihood ratio tests, and several empirical likelihood confidence band results. While survival analysis is a classic area of statistical study, the empirical likelihood methodology has only recently been developed. Until now, just one book was available on empirical likelihood and most statistical software did not include empirical likelihood procedures. Addressing this shortfall, this book provides the functions to calculate the empirical likelihood ratio in survival analysis as well as functions related to the empirical likelihood analysis of the Cox regression model and other hazard regression models.

Ideas about the transmission of disease have long formed the core of modern biology and medicine. Heredity and Infection examines their development over the last century. Two scientific revolutions - the bacteriological revolution of the 1890s and the genetic revolution at the start of the twentieth century - acted as the catalysts of major change in our understanding of the causes of illness. As well as being great scientific achievements, these were social and political watersheds that reconfigured the medical and administrative means of intervention. By establishing a clear distinction between transmission by infection and genetic transmission, this shift was instrumental in separating hygiene from eugenism. The authors argue that the popular perception of such a sharp divide stabilized only after 1945 when the use of antibiotics to end epidemics became commonplace. For health professionals the separation has never become an absolute one, and the book examines the various blends of heredity and infection that have preoccupied biology, medicine and the social sciences. Heredity and Infection recontructs the changing epidemiology of such historically important pathologies as tuberculosis , cancer and AIDS. In doing so, it demonstrates the role of experimental models, medical practices and cultural images in the making of contemporary biochemical knowledge.

Epidemiology is integral to public health. This book introduces the principles, methods and application of epidemiology for improving health and survival. It is designed for self-directed learning by students and all who work in public health and health-related areas, including health economists, health policy analysts, and health services managers. Using this book will help you to practice the application of basic epidemiological methods to measure health outcomes, identify risk factors for a negative outcome, and evaluate health interventions and health services. The book helps to distinguish between strong and poor epidemiological evidence, an ability that is fundamental to promoting evidence-based health care. This 3rd edition has been revised to include: * A new section on the historical development of epidemiology * New infographics and figures to help visualise concepts * Contemporary health issues explored through examples and exercises * More activities for self-testing * A new final integrating chapter with real-life examples, such as the Zika outbreak, linking research to implementation Introduction to Epidemiology 3rd edition is an essential resource on a fascinating area that is crucial to an understanding of public health. Series Editors: Rosalind Plowman and Nicki Thorogood.

Statistical Design, Monitoring, and Analysis of Clinical Trials, Second Edition concentrates on the biostatistics component of clinical trials. This new edition is updated throughout and includes five new chapters. Developed from the authors' courses taught to public health and medical students, residents, and fellows during the past 20 years, the text shows how biostatistics in clinical trials is an integration of many fundamental scientific principles and statistical methods. The book begins with ethical and safety principles, core trial design concepts, the principles and methods of sample size and power calculation, and analysis of covariance and stratified analysis. It then focuses on sequential designs and methods for two-stage Phase II cancer trials to Phase III group sequential trials, covering monitoring safety, futility, and efficacy. The authors also discuss the development of sample size reestimation and adaptive group sequential procedures, phase 2/3 seamless design and trials with predictive biomarkers, exploit multiple testing procedures, and explain the concept of estimand, intercurrent events, and different missing data processes, and describe how to analyze incomplete data by proper multiple imputations. This text reflects the academic research, commercial development, and public health aspects of clinical trials. It gives students and practitioners a multidisciplinary understanding of the concepts and techniques involved in designing, monitoring, and analyzing various types of trials. The book's balanced set of homework assignments and in-class exercises are appropriate for students and researchers in (bio)statistics, epidemiology, medicine, pharmacy, and public health.

Health statistics have been an essential tool for improving the health of populations for centuries, yet no single book covers the key elements in developing, using, and improving them. This volume fills that crucial gap by providing a comprehensive account of the essential concepts and complex underpinnings of health statistics. It gives a broad and detailed view of the sources and uses of health statistics and explores contemporary issues confronting the health statistics enterprise, including privacy, technology, and the emergence of health data standards. It also proposes fundamental changes needed to improve health statistics that can be embraced by practitioners at all levels of government and the private sector. The book is guided throughout by a comprehensive model of population health that expands the traditionally held view of what factors influence health. The chapters are grouped into five sections: 1) defining health statistics-context, history, and organization; 2) collecting and compiling health statistics; 3) putting health statistics to use; 4) identifying current and forthcoming issues and 5) transforming health statistics through new conceptual frameworks. This logical organization helps make the book suitable for graduate courses in public health and public health surveillance, health services research, population health statistics, or population health information systems. It will be equally useful for the staff of the many organizations that comprise the health statistics enterprise, for health professionals seeking a broader context for their efforts, and for researchers aiming to advance the field of health statistics and their application to health policy or public health practice.

An introduction to state-of-the-art modeling and simulation approaches for social and economic determinants of population health New Horizons in Modeling and Simulation for Social Epidemiology and Public Health offers a comprehensive introduction to modeling and simulation that addresses the many complex research questions in social epidemiology and public health. This book highlights a variety of practical applications and illustrative examples with a focus on modeling and simulation approaches for the social and economic determinants of population health. The book contains classic case examples in agent-based modeling (ABM) as well as essential information on ABM applications to public health including for infectious disease modeling, obesity, and tobacco control. This book also surveys applications of microsimulation (MSM) including of tax-benefit policies to project impacts of the social determinants of health. Specifically, this book: Provides an overview of the social determinants of health and the public health significance of addressing the social determinants of health Gives a conceptual foundation for the application of ABM and MSM to study the social determinants of health Offers methodological introductions to both ABM and MSM approaches with illustrative examples Includes cutting-edge systematic reviews of empirical applications of ABM and MSM in the social sciences, social epidemiology, and public health Discusses future directions for empirical research using ABM and MSM, including integrating aspects of both ABM and MSM and implications for public health policies Written for a broad audience of policy analysts, public planners, and researchers and practitioners in public health and public policy including social epidemiologists, New Horizons in Modeling and Simulation for Social Epidemiology and Public Health offers a fundamental guide to the social determinants of health and state-of-the-art applications of ABM and MSM to studying the social and economic determinants of population health.

Now that the health community is in a state of reflection, how do we put the lessons learned into practice? As we step back to examine the worldwide response to the COVID-19 pandemic, now is the time to think about how to raise the bar for our response to the next public health emergency. Now is the time to revisit health preparedness strategies and plans. And now is the time to review what the health community did that worked-and how we can do that again. Learning from COVID-19: GIS for Pandemics tells real-life stories about how spatial thinking became invaluable in both local and full-scale outbreaks during the COVID-19 pandemic. Needing to answer the question of "where" sat at the forefront of everyone's mind, and using a geographic information system (GIS) for real-time surveillance transformed possibly overwhelming data into location intelligence that provided agencies and civic leaders with valuable insights. Co-edited by Esri chief medical officer Dr. Este Geraghty, this book highlights best practices, key GIS capabilities, and lessons learned during the COVID-19 response that can help communities prepare for the next crisis. GIS has empowered: Organizations to use human mobility data to estimate the adherence to social distancing guidelines Communities to monitor their health care systems' capacity through spatially enabled surge tools Governments to use location-allocation methods to site new resources (i.e., testing sites and augmented care sites) in ways that account for at-risk and vulnerable populations Communities to use maps and spatial analysis to review case trends at local levels to support reopening of economies Organizations to think spatially as they consider "back-to-the-workplace" plans that account for physical distancing and employee safety needs Learning from COVID-19 also includes a "next steps" section that provides ideas, strategies, tools, and actions to help jump-start your own use of GIS, either as a citizen scientist or a health professional. A collection of online resources, including additional stories, videos, new ideas and concepts, and downloadable tools and content, complements this book. Now is the time to use science and data to make informed decisions for our future, and this book shows us how we can do it.

It has now been 25 years since the apocryphal report in the CDC Morbidity and Mortality Weekly Report dated June 5, 1981 entitled, "Pneumocystis Pneumonia - Los Angeles", which announced what was to become HIV/AIDS. HIV has now affected virtually all countries that have looked for it and has had a devastating impact on the public health and medical care infrastructure around the world. HIV/AIDS has also disproportionately affected nations with the least capacity to confront it, especially the developing world nations in Sub-Saharan Africa, South and Southeast Asia, and the emerging republics of Eastern and Central Asia. The pandemic, unlike any other disease of our time, has had profound impacts on the practice of public health itself: bringing affected communities into decision making; demanding North-South partnerships and collaborations; and changing the basic conduct of clinical and prevention trials research. While much has been written in scholarly publications for medical, epidemiologic and disease control specialists, there is no comprehensive review of the public health impact and response to HIV/AIDS in the developing world. This edited volume seeks to systematically describe the emergence and form of the epidemics (epidemiology), the social, community and political response, and the various measures to confront and control the epidemic, with varying levels of success. Of particular importance are strategies that appear to have been useful in ameliorating the epidemic, while contrasting the situation in a neighboring country or region where contrasting prevention or care initiatives have had a deleterious outcome. Common to all responses has been the international multi-sectoral response represented by the Global Fund for HIV/AIDS, Malaria and Tuberculosis, the President's Emergency Plan for AIDS Relief, and the Gates Foundation, among others, to promote HIV pharmacologic therapy in resource-poor settings. The chapter authors will explore the political challenges in meeting HIV/AIDS prevention and care in concert with the public health realities in specific country and regional context.

This book provides an integrated description of methods used to rear vectors of human, higher animal, and plant pathogens in the laboratory. It deals with diverse subject areas, and contains descriptions of standard, as well as highly specialized, methods used by medical, veterinary, entomology, and plant pathology experts. The text brings together the standard breeding and manipulation methods developed in America, Europe, Asia, and Africa. It describes the cultivating, handling, sterile techniques, and cell culture as well as safety measures to prevent contamination and escape of insects, ticks, nematodes and fungal vectors.

This book describes key methods and instruments for assessing diet-related factors, physical activity, social and environmental factors, physical characteristics and health-related outcomes in children and adolescents. These tools were developed and deployed within the framework of the pan-European IDEFICS and I.Family cohort studies. These population-based field studies were funded within the 6th and 7th European Framework Programme, respectively, and were intended to assess the prevalence and aetiology of lifestyle-related diseases in children, focusing on overweight and obesity, and to develop effective strategies for primary prevention. In the course of a decade we undertook a major research endeavour, collecting standardised data from children, families, neighbourhoods, kindergartens, pre-schools and schools in eight European countries, employing a uniform cross-cultural methodology. This resulted in a rich picture of the daily lives and living contexts of children and their families. Studies encompassing childhood and adolescence face the particular challenge of the transitions from pre-school to primary school and from childhood to adolescence; accordingly, the instruments used need to be adapted to different developmental stages while maintaining their comparability across the age range. In young children, questionnaires have to be completed by proxies, usually their parents, while older children, particularly adolescents, can provide a major part of the requested information themselves. This book presents suitable designs, methods and instruments for data collection in studies of children and adolescents. Each chapter explains the development and background of the instruments applied in the surveys and summarises the current state of knowledge. All chapters were written by key experts in their respective research fields. We are grateful for their valuable contributions and their enthusiastic support in producing this book, which also presents survey experiences in which practice does not always follow theory. Participants' responses can on occasion be unexpected and unpredictable, but meeting these challenges can also enrich epidemiological surveys and yield methodological refinements. We sincerely hope that the book and the online material will be of considerable value to other research teams.

Theory of Drug Development presents a formal quantitative framework for understanding drug development that goes beyond simply describing the properties of the statistics in individual studies. It examines the drug development process from the perspectives of drug companies and regulatory agencies. By quantifying various ideas underlying drug development, the book shows how to systematically address problems, such as: Sizing a phase 2 trial and choosing the range of p-values that will trigger a follow-up phase 3 trial Deciding whether a drug should receive marketing approval based on its phase 2/3 development program and recent experience with other drugs in the same clinical area Determining the impact of adaptive designs on the quality of drugs that receive marketing approval Designing a phase 3 pivotal study that permits the data-driven adjustment of the treatment effect estimate Knowing when enough information has been gathered to show that a drug improves the survival time for the whole patient population Drawing on his extensive work as a statistician in the pharmaceutical industry, the author focuses on the efficient development of drugs and the quantification of evidence in drug development. He provides a rationale for underpowered phase 2 trials based on the notion of efficiency, which leads to the identification of an admissible family of phase 2 designs. He also develops a framework for evaluating the strength of evidence generated by clinical trials. This approach is based on the ratio of power to type 1 error and transcends typical Bayesian and frequentist statistical analyses.

With Sweden traditionally hailed as a social and economic model, it is no wonder that the Swedish response to the COVID-19 pandemic raised a lot of questions - and eyebrows - around the world. This short book explores Sweden's unique response to the global pandemic and the strong wave of controversies it triggered. It helps to make sense of the response by defining 'a Swedish model' that incorporates the country's value system, underpinning its politics and administration in relation to, among other things, welfare, democracy, civil liberties and respect for expertise. The book also acts as a case study for understanding the moral and normative ways in which different national approaches to the pandemic have been compared.

Biostatistics for Clinical and Public Health Research provides a concise overview of statistical analysis methods. Use of SAS and Stata statistical software is illustrated in full, including how to interpret results. Focusing on statistical models without all the theory, the book is complete with exercises, case studies, take-away points, and data sets. Readers will be able to maximize their statistical abilities in hypothesis testing, data interpretation, and application while also learning when and how to consult a biostatistician. This book will be an invaluable tool for students and clinical and public health practitioners.

Examines psychiatric epidemiology's unique evolution, conceptually and socially, within and between diverse regions and cultures, underscoring its growing influence on the biopolitics of nations and worldwide health campaigns. Psychiatric epidemiology, like the epidemiology of cancer, heart disease, or AIDS, contributes increasingly to shaping the biopolitics of nations and worldwide health campaigns. Despite the field's importance, this is the first volume of historical scholarship addressing psychiatric epidemiology. It seeks to comprehensively trace the development of the discipline and the mobilization of its constructs, methods, and tools to further social ends. It is through this double lens-conceptual and social-that it envisions the history of psychiatric epidemiology. Furthermore, its chapters constitute elements for that history as a global phenomenon, formed by multiple approaches. Those numerous historical paths have not resulted in a uniform disciplinary field based on a common paradigm, as happened arguably in the epidemiology of cardiovascular disease and cancer, but in a plurality of psychiatric epidemiologies driven by different intellectual questions, political strategies, reformist ideals, national cultures, colonial experiences, international influences, and social control objectives. When examined together, the chapters depict an uneven global development of epidemiologies formed within distinct political-cultural regions but influenced by the transnational circulation and selective uptake of concepts, techniques, and expertise. These moved through multidirectional pathways between and within the Global North and South. Authored by historians, anthropologists, and psychiatrists, chapters trace this complex history, focusing on Brazil, Nigeria, Senegal, India, Taiwan, Japan, the United Kingdom, the United States and Canada, as well as multicountry networks.

This is a comprehensive, practical guide which looks at the advantages and limitations of new data analysis techniques being introduced across public health and administration services. The Affordable Care Act (ACT) and free market reforms in healthcare are generating a rapid change of pace. The "electronification" of medical records from paper to digital, which is required to meet the meaningful use standards set forth by the Act, is advancing what and how information can be analyzed. Coupled with the advent of more computing power and big data analytics and techniques, practitioners now more than ever need to stay on top of these trends. This book presents a comprehensive look at healthcare analytics from population data to geospatial analysis using current case studies and data analysis examples in health. This resource will appeal to undergraduate and graduate students in health administration and public health. It will benefit healthcare professionals and administrators in nursing and public health, as well as medical students who are interested in the future of data within healthcare.

The EQ-5D is one of the most widely-used generic health state descriptive systems internationally, with applications in clinical trials, public health research and assessments of value for money. In addition to providing a way of describing health and health improvement, the EQ-5D facilitates the valuation of health and health gain through its pre-existing value sets, spanning a wide range of countries and continents. This book brings together, for the first time, a comprehensive inventory of these value sets and their characteristics.

Despite the growing interest in the role of psychological trauma in the genesis of psychiatric disorders, few volumes have addressed these issues from a multidisciplinary and international perspective. Given the complexity of reslience and posttraumatic disorder, and given ongoing trauma and violence in many parts of the world, it is crucial to apply such perspectives to review existing knowledge in the field and provide directions for future research. This book has a broad scope. A key focus is PTSD, because of its clinical and health importance, its obvious link with trauma, and its interest for many clinicians and researchers. However, the book also examines resilience and a range of mental health consequences of trauma, because it has become increasingly clear that not all individuals react to trauma in the same way. It is important for mental health professionals to be aware of the broad range of potential responses to trauma, as well as of relevant evidence-based treatments. The book includes chapters that address a wide range of topics on trauma-related disorders, including nosology and classification, epidemiology, neurobiology, pharmacotherapy, and psychotherapy. Each chapter comprises a critical review of the existing literature, aimed at being useful for the practitioner. This is followed by selected commentaries from other authorities on the topic, representing diverse geographical locations and points of view, who refine some of the perspectives offered in the review, provide alternative views, or suggest important areas of future work.

Many new challenges have arisen in the area of oncology clinical trials. New cancer therapies are often based on cytostatic or targeted agents, which pose new challenges in the design and analysis of all phases of trials. The literature on adaptive trial designs and early stopping has been exploding. Inclusion of high-dimensional data and imaging techniques have become common practice, and statistical methods on how to analyse such data have been refined in this area. A compilation of statistical topics relevant to these new advances in cancer research, this third edition of Handbook of Statistics in Clinical Oncology focuses on the design and analysis of oncology clinical trials and translational research.

Addressing the many challenges that have arisen since the publication of its predecessor, this third edition covers the newest developments involved in the design and analysis of cancer clinical trials, incorporating updates to all four parts:

• Phase I trials: Updated recommendations regarding the standard 3 + 3 and continual reassessment approaches, along with new chapters on phase 0 trials and phase I trial design for targeted agents.
• Phase II trials Updates to current experience in single-arm and randomized phase II trial designs. New chapters include phase II designs with multiple strata and phase II/III designs.
• Phase III trials Many new chapters include interim analyses and early stopping considerations, phase III trial designs for targeted agents and for testing the ability of markers, adaptive trial designs, cure rate survival models, statistical methods of imaging, as well as a thorough review of software for the design and analysis of clinical trials.
• Exploratory and high-dimensional data analyses All chapters in this part have been thoroughly updated since the last edition. New chapters address methods for analyzing SNP data and for developing a score based on gene expression data. In addition, chapters on risk calculators and forensic bioinformatics have been added.

Accessible to statisticians and oncologists interested in clinical trial methodology, the book is a single-source collection of up-to-date statistical approaches to research in clinical oncology.

In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life; nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology is now an integral part of the business of healthcare. Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care. R for Health Data Science includes everything a healthcare professional needs to go from R novice to R guru. By the end of this book, you will be taking a sophisticated approach to health data science with beautiful visualisations, elegant tables, and nuanced analyses. Features Provides an introduction to the fundamentals of R for healthcare professionals Highlights the most popular statistical approaches to health data science Written to be as accessible as possible with minimal mathematics Emphasises the importance of truly understanding the underlying data through the use of plots Includes numerous examples that can be adapted for your own data Helps you create publishable documents and collaborate across teams With this book, you are in safe hands - Prof. Harrison is a clinician and Dr. Pius is a data scientist, bringing 25 years' combined experience of using R at the coal face. This content has been taught to hundreds of individuals from a variety of backgrounds, from rank beginners to experts moving to R from other platforms.