This book aims to fill a gap in the current construction health and safety research and discover new knowledge about work stress induced chronic diseases among construction industry professionals. In achieving these aims, the book investigates: the nature and extent of psychosocial stressors experienced by construction professionals, stress management tactics applied and the impact on mental health the prevalence and occurrence patterns of serious chronic conditions such as insomnia, obesity, musculoskeletal disorders and vision impairment aetiological pathways from job stressors through chronic diseases to job performance. While there are many studies, policies and regulations aiming to look after the health of construction workers, little attention is paid to construction professionals. By applying advanced analytical methods to data collected in a national survey of construction professionals in Australia, the author presents new scientific evidence which can be used to help establish equitable workers' compensation treatments and outcomes for construction professionals in line with other professions. Moreover, the research and analysis are underpinned by theories and literature from public health and epidemiological disciplines in addition to literature from construction, and work health, safety and wellbeing domains. It is essential reading for any health policy makers and researchers in the fields of health and safety and construction management.

This book explores a range of biohealth and biosecurity threats, places them in context, and offers responses and solutions from global and local, networked and pyramidal, as well as specialized and interdisciplinary perspectives. Specifically covering bioterrorism, emerging infectious diseases, pandemic disease preparedness and remediation, agroterroism, food safety, and environmental issues, the contributors demonstrate that to counter terrorism of any kind, a global, networked, and multidisciplinary approach is essential. To be successful in biosecurity, this book argues it is necessary to extend partnerships, cooperation, and co-ordination between public health, clinical medicine, private business, law enforcement and other agencies locally, nationally and internationally. Internationally, a clear understanding is needed of what has happened in past epidemics and what was accomplished in past bioprograms (in Britain, South Africa, Russia, for example). This book also assesses how, with the right technology and motivation, both a state and a non-state actor could initiate an extremely credible biothreat to security at both local and national levels. This book will be of much interest to students, researchers and practitioners of security studies, public health, public policy and IR in general. Peter Katona is Associate Professor of Clinical Medicine at the David Geffen School of Medicine at UCLA in Infectious Diseases. He is co-founder of Biological Threat Mitigation, a bioterror consulting firm. John P. Sullivan is a lieutenant with the Los Angeles Sheriff's Department. He is also a researcher focusing on terrorism, conflict disaster, intelligence studies, and urban operations. He is co-founder of the Los Angeles Terrorism Early Warning (TEW) Group. Michael D. Intriligator is Professor of Economics at the University of California, Los Angeles (UCLA). He is also Professor of Political Science, Professor of Public Policy in the School of Public Policy and Social Research, and Co-Director of the Jacob Marschak Interdisciplinary Colloquium on Mathematics in the Behavioral Sciences, all at UCLA.

Ebola: Clinical Patterns, Public Health Concerns is a concise description and discussion of the Ebola virus and disease. The intended audience is medical practitioners, including those working in endemic areas as well as health-facility planners and public health practitioners. The book fills an important gap between large texts covering not only Ebola but other hemorrhagic fever viruses and brief pamphlet-style publications on the public health aspects of the infection. In light of the recent large outbreak in West Africa, this book is a part of the developing foundation needed to deal with emerging diseases.

Introductory Statistics for the Health Sciences takes students on a journey to a wilderness where science explores the unknown, providing students with a strong, practical foundation in statistics. Using a color format throughout, the book contains engaging figures that illustrate real data sets from published research. Examples come from many areas of the health sciences, including medicine, nursing, pharmacy, dentistry, and physical therapy, but are understandable to students in any field. The book can be used in a first-semester course in a health sciences program or in a service course for undergraduate students who plan to enter a health sciences program. The book begins by explaining the research context for statistics in the health sciences, which provides students with a framework for understanding why they need statistics as well as a foundation for the remainder of the text. It emphasizes kinds of variables and their relationships throughout, giving a substantive context for descriptive statistics, graphs, probability, inferential statistics, and interval estimation. The final chapter organizes the statistical procedures in a decision tree and leads students through a process of assessing research scenarios. Web ResourceThe authors have partnered with William Howard Beasley, who created the illustrations in the book, to offer all of the data sets, graphs, and graphing code in an online data repository via GitHub. A dedicated website gives information about the data sets and the authors' electronic flashcards for iOS and Android devices. These flashcards help students learn new terms and concepts.

Judith Lorber and Lisa Jean Moore consider the interface between the social institutions of gender and Western medicine in this brief, lively textbook. They offer a distinct feminist viewpoint to analyze issues of power and politics concerning physical illness. SIGNS labeled the first edition 'a rich and imaginative work.' In the extensively revised second edition of this successful text, the authors add chapters on disability and genital surgeries. They also update and expand their discussions of social epidemiology, AIDS, the health professions, PMS, menopause, and feminist health care. For a creative, feminist-oriented alternative to traditional texts on medical sociology, medical anthropology, and the history of medicine, this is an ideal choice.

The worldwide epidemiology of rickettsial diseases, including veterinary rickettsiology, is the focus of this volume, which is the second volume resulting from the 4th International Conference on Rickettsiae and Rickettsial Diseases.

The heterogeneity of clinical presentations of rickettsial diseases is greater than had previously been appreciated, and atypical and previously unseen presentations are discussed here along with chapters on known vectors and vectors previously not known to transmit a given rickettsial species.

This volume and volume 1063, "New Insights into Rickettsioses," complete the picture presented at the conference of the wide range of work that is being carried out in rickettsiology worldwide.

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The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. Frailty Models in Survival Analysis presents a comprehensive overview of the fundamental approaches in the area of frailty models. The book extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of univariate and shared frailty models. The author analyzes similarities and differences between frailty and copula models; discusses problems related to frailty models, such as tests for homogeneity; and describes parametric and semiparametric models using both frequentist and Bayesian approaches. He also shows how to apply the models to real data using the statistical packages of R, SAS, and Stata. The appendix provides the technical mathematical results used throughout. Written in nontechnical terms accessible to nonspecialists, this book explains the basic ideas in frailty modeling and statistical techniques, with a focus on real-world data application and interpretation of the results. By applying several models to the same data, it allows for the comparison of their advantages and limitations under varying model assumptions. The book also employs simulations to analyze the finite sample size performance of the models.

This book provides an engaging, jargon-free introduction to the threat of global pandemics, offering an overview of the many origins and triggers of pandemic events. It covers the impacts generated by novel infectious disease outbreaks across various dimensions - from social and ethical to medical and political, from media to economic and legal implications. The author discusses the preparedness strategies developed globally, the lessons learned from various outbreaks and the mitigation measures deployed - from quarantine and social distancing to data sharing and surveillance systems - including their unintended impacts. While the risk of global pandemics is certainly intensely debated by the scientific community, and increasingly by policy makers at various levels, the threat is hardly discussed in the public domain. It only permeates the media during crisis events, such as during the SARS outbreak in 2003, the West African Ebola outbreak in 2014-15, and most notably the ongoing COVID-19 global pandemic crisis. This book is thus highly timely and topical. It has a global scope, whilst at times zooming in on the implications of pandemic risk and mitigation for the Global North or the Global South. Given the interdisciplinarity of the topic, this book will be of great interest to a wider non-academic audience, as well as students from a range of subjects including politics, sociology, geography, anthropology, and international development, along with entry-level medical students keen to widen their appreciation of the social dimensions of the medical work they set out to conduct.

Recent disease events such as SARS, H1N1 and avian influenza, and haemorrhagic fevers have focussed policy and public concern as never before on epidemics and so-called 'emerging infectious diseases'. Understanding and responding to these often unpredictable events have become major challenges for local, national and international bodies. All too often, responses can become restricted by implicit assumptions about who or what is to blame that may not capture the dynamics and uncertainties at play in the multi-scale interactions of people, animals and microbes. As a result, policies intended to forestall epidemics may fail, and may even further threaten health, livelihoods and human rights. The book takes a unique approach by focusing on how different policy-makers, scientists, and local populations construct alternative narratives-accounts of the causes and appropriate responses to outbreaks- about epidemics at the global, national and local level. The contrast between emergency-oriented, top-down responses to what are perceived as potentially global outbreaks and longer-term approaches to diseases, such as AIDS, which may now be considered endemic, is highlighted. Case studies-on avian influenza, SARS, obesity, H1N1 influenza, HIV/AIDS, tuberculosis, and haemorrhagic fevers-cover a broad historical, geographical and biological range. As this book explores, it is often the most vulnerable members of a population-the poor, the social excluded and the already ill-who are likely to suffer most from epidemic diseases. At the same time, they may be less likely to benefit from responses that may be designed from a global perspective that neglects social, ecological and political conditions on the ground. This book aims to bring the focus back to these marginal populations to reveal the often unintended consequences of current policy responses to epidemics. Important implications emerge - for how epidemics are thought about and represented; for how surveillance and response is designed; and for whose knowledge and perspectives should be included. Published in association with the Economic and Social Research Council (ESRC)

Over the past decade, substantial resources have been spent on tackling avian influenza and building a global capacity for a pandemic response. The catastrophic costs of the 1918 influenza pandemic are well documented, and the swine flu pandemic of 2009-10 has raised the alarm yet again. Across the world, surveillance systems have been upgraded, stockpiles of antiviral drugs and influenza vaccines have been created, veterinary and public health systems have been improved and poultry production and marketing has been dramatically restructured. What are the lessons from this experience? And what does this suggest for the future? This book explores how virus genetics, ecology and epidemiology intersect with economic, political and policy processes in a variety of places - from Bangkok to Washington, to Jakarta, Cairo, Rome and London. It focuses on the interaction of the international and national responses - and in particular the experiences of Cambodia, Vietnam, Indonesia and Thailand. It asks how effective is the disease surveillance and response system - can it respond to a new pandemic threat? The comparative analysis reveals the challenges and limitations of a technocratic, centralised response, and the need to take seriously local contexts. Drawing from these experiences, the book concludes with a discussion of future prospects and challenges, examining in particular what a 'One World, One Health' approach - where approaches to animal, human and ecosystem health are integrated - would look like in practice. Published in association with the Economic and Social Research Council (ESRC).

Teaches Students How to Perform Spatio-Temporal Analyses within Epidemiological Studies Spatio-Temporal Methods in Environmental Epidemiology is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. In response to the growing need for collaboration between statisticians and environmental epidemiologists, the book links recent developments in spatio-temporal methodology with epidemiological applications. Drawing on real-life problems, it provides the necessary tools to exploit advances in methodology when assessing the health risks associated with environmental hazards. The book's clear guidelines enable the implementation of the methodology and estimation of risks in practice. Designed for graduate students in both epidemiology and statistics, the text covers a wide range of topics, from an introduction to epidemiological principles and the foundations of spatio-temporal modeling to new research directions. It describes traditional and Bayesian approaches and presents the theory of spatial, temporal, and spatio-temporal modeling in the context of its application to environmental epidemiology. The text includes practical examples together with embedded R code, details of specific R packages, and the use of other software, such as WinBUGS/OpenBUGS and integrated nested Laplace approximations (INLA). A supplementary website provides additional code, data, examples, exercises, lab projects, and more. Representing a major new direction in environmental epidemiology, this book-in full color throughout-underscores the increasing need to consider dependencies in both space and time when modeling epidemiological data. Students will learn how to identify and model patterns in spatio-temporal data as well as exploit dependencies over space and time to reduce bias and inefficiency.

This book analyzes the development of female prostitution in the Pacific port of Puntarenas, Costa Rica during the advanced stage of the coffee exporting economy (1880-1930), at the height of the consolidation of the liberal state. Hayes argues that prostitution in the port differed from that of the coffee producing highlands due to differential economic, social, and political development. In the periphery of Puntarenas, the development of prostitution reflected a less stigmatized view of sexual commerce than that of the highlands, where prostitution, although legal, threatened the tenets of liberal nationalism based on racial homogeneity and family values. Women of the highlands were encouraged to reproduce the nation's "more European" stock of workers and to ensure the legal transference of property through legal church marriages - both part of a design to stabilize the coffee exporting project. By contrast, prostitutes and other working women of Puntarenas, many immigrants from the "less European" populations of neighboring regions and most in concubinage, were freer to do what the law prescribed - register as prostitutes in legitimate trade. Such regional disparities reveal weaknesses in traditional explanations of Costa Rican exceptionalism, which have rested on the premise of cultural homogeneity and have reflected the realities of only one region of the country. The book advances an alternative explanation for the development of the nation's more democratic institutions, situating Costa Rican exceptionalism in the nation's free labor system, of which the labor prostitute in Puntarenas provides an example.

Over the past two decades, rates of adult and childhood obesity in the developed world have risen sharply. By the year 2000, 65% of the United States population were overweight, 30% of these obese. Whilst medical treatment has tended to focus on individual habits of diet and exercise, this approach does little to account for globally increasing levels of obesity, and the external, environmental factors that may be responsible. This in-depth study assembles the evidence for a geographical explanation of current obesity trends, and is the first work to examine the ways in which environment and living conditions promote an imbalance of energy intake over energy expenditure. The book calls upon the expertise of geographers, nutritionists, epidemiologists, sociologists and public health researchers, resulting in a broad, multidisciplinary analysis of this important health issue. Cover graphic designed by Georgia Witten-Sage.

The premise of Quality by Design (QbD) is that the quality of the pharmaceutical product should be based upon a thorough understanding of both the product and the manufacturing process. This state-of-the-art book provides a single source of information on emerging statistical approaches to QbD and risk-based pharmaceutical development. A comprehensive resource, it combines in-depth explanations of advanced statistical methods with real-life case studies that illustrate practical applications of these methods in QbD implementation.

This book explores a range of biohealth and biosecurity threats, places them in context, and offers responses and solutions from global and local, networked and pyramidal, as well as specialized and interdisciplinary perspectives.

Specifically covering bioterrorism, emerging infectious diseases, pandemic disease preparedness and remediation, agroterroism, food safety, and environmental issues, the contributors demonstrate that to counter terrorism of any kind, a global, networked, and multidisciplinary approach is essential. To be successful in biosecurity, this book argues it is necessary to extend partnerships, cooperation, and co-ordination between public health, clinical medicine, private business, law enforcement and other agencies locally, nationally and internationally. Internationally, a clear understanding is needed of what has happened in past epidemics and what was accomplished in past bioprograms (in Britain, South Africa, Russia, for example). This book also assesses how, with the right technology and motivation, both a state and a non-state actor could initiate an extremely credible biothreat to security at both local and national levels.

This book will be of much interest to students, researchers and practitioners of security studies, public health, public policy and IR in general.

Peter Katona is Associate Professor of Clinical Medicine at the David Geffen School of Medicine at UCLA in Infectious Diseases. He is co-founder of Biological Threat Mitigation, a bioterror consulting firm.

John P. Sullivan is a lieutenant with the Los Angeles Sheriff's Department. He is also a researcher focusing on terrorism, conflict disaster, intelligence studies, and urban operations. He is co-founder of the Los Angeles Terrorism Early Warning (TEW) Group.

Michael D. Intriligator is Professor of Economics at the University of California, Los Angeles (UCLA). He is also Professor of Political Science, Professor of Public Policy in the School of Public Policy and Social Research, and Co-Director of the Jacob Marschak Interdisciplinary Colloquium on Mathematics in the Behavioral Sciences, all at UCLA.

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.

The SARS-CoV-2 virus, and the associated COVID-19 pandemic, is perhaps the greatest threat to life, and lifestyles, the world has known in more than a century. The scholarship included here provides critical insights into the institutional responses, communal consequences, cultural adaptations, and social politics that lie at the heart of this pandemic. This volume maps out the ways in which the pandemic has impacted (most often disproportionately) societies, the successes and failures of means used to combat the virus, and the considerations and future possibilities - both positive and negative - that lie ahead. While the pandemic has brought humanity together in some noteworthy ways, it has also laid bare many of the systemic inequalities that lie at the foundation of our global society. This volume is a significant step toward better understanding these impacts. The work presented here represents a remarkable diversity and quality of impassioned scholarship and is a timely and critical advance in knowledge related to the pandemic. This volume and its companion, COVID-19: Volume I: Global Pandemic, Societal Responses, Ideological Solutions, are the result of the collaboration of more than 50 of the leading social scientists from across five continents. The breadth and depth of the scholarship is matched only by the intellectual and global scope of the contributors themselves. The insights presented here have much to offer not just to an understanding of the ongoing world of COVID-19, but also to helping us (re-) build, and better shape, the world beyond.

The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women's Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.

Analyzing Health Data in R for SAS Users is aimed at helping health data analysts who use SAS accomplish some of the same tasks in R. It is targeted to public health students and professionals who have a background in biostatistics and SAS software, but are new to R. For professors, it is useful as a textbook for a descriptive or regression modeling class, as it uses a publicly-available dataset for examples, and provides exercises at the end of each chapter. For students and public health professionals, not only is it a gentle introduction to R, but it can serve as a guide to developing the results for a research report using R software. Features: Gives examples in both SAS and R Demonstrates descriptive statistics as well as linear and logistic regression Provides exercise questions and answers at the end of each chapter Uses examples from the publicly available dataset, Behavioral Risk Factor Surveillance System (BRFSS) 2014 data Guides the reader on producing a health analysis that could be published as a research report Gives an example of hypothesis-driven data analysis Provides examples of plots with a color insert

Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials is the first book focused on the application of generalized linear mixed models and its related models in the statistical design and analysis of repeated measures from randomized controlled trials. The author introduces a new repeated measures design called S:T design combined with mixed models as a practical and useful framework of parallel group RCT design because of easy handling of missing data and sample size reduction. The book emphasizes practical, rather than theoretical, aspects of statistical analyses and the interpretation of results. It includes chapters in which the author describes some old-fashioned analysis designs that have been in the literature and compares the results with those obtained from the corresponding mixed models. The book will be of interest to biostatisticians, researchers, and graduate students in the medical and health sciences who are involved in clinical trials. Author Website:Data sets and programs used in the book are available at http://www.medstat.jp/downloadrepeatedcrc.html

Comparative effectiveness research (CER) is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care (IOM 2009). CER is conducted to develop evidence that will aid patients, clinicians, purchasers, and health policy makers in making informed decisions at both the individual and population levels. CER encompasses a very broad range of types of studies-experimental, observational, prospective, retrospective, and research synthesis. This volume covers the main areas of quantitative methodology for the design and analysis of CER studies. The volume has four major sections-causal inference; clinical trials; research synthesis; and specialized topics. The audience includes CER methodologists, quantitative-trained researchers interested in CER, and graduate students in statistics, epidemiology, and health services and outcomes research. The book assumes a masters-level course in regression analysis and familiarity with clinical research.

Cost-effectiveness analysis is becoming an increasingly important tool for decision making in the health systems. Cost-Effectiveness of Medical Treatments formulates the cost-effectiveness analysis as a statistical decision problem, identifies the sources of uncertainty of the problem, and gives an overview of the frequentist and Bayesian statistical approaches for decision making. Basic notions on decision theory such as space of decisions, space of nature, utility function of a decision and optimal decisions, are explained in detail using easy to read mathematics. Features Focuses on cost-effectiveness analysis as a statistical decision problem and applies the well-established optimal statistical decision methodology. Discusses utility functions for cost-effectiveness analysis. Enlarges the class of models typically used in cost-effectiveness analysis with the incorporation of linear models to account for covariates of the patients. This permits the formulation of the group (or subgroup) theory. Provides Bayesian procedures to account for model uncertainty in variable selection for linear models and in clustering for models for heterogeneous data. Model uncertainty in cost-effectiveness analysis has not been considered in the literature. Illustrates examples with real data. In order to facilitate the practical implementation of real datasets, provides the codes in Mathematica for the proposed methodology. The motivation for the book is to make the achievements in cost-effectiveness analysis accessible to health providers, who need to make optimal decisions, to the practitioners and to the students of health sciences. Elias Moreno is Professor of Statistics and Operational Research at the University of Granada, Spain, Corresponding Member of the Royal Academy of Sciences of Spain, and elect member of ISI. Francisco Jose Vazquez-Polo is Professor of Mathematics and Bayesian Methods at the University of Las Palmas de Gran Canaria, and Head of the Department of Quantitative Methods. Miguel Angel Negrin is Senior Lecturer in the Department of Quantitative Methods at the ULPGC. His main research topics are Bayesian methods applied to Health Economics, economic evaluation and cost-effectiveness analysis, meta-analysis and equity in the provision of healthcare services.

Develop Effective Immunogenicity Risk Mitigation Strategies Immunogenicity assessment is a prerequisite for the successful development of biopharmaceuticals, including safety and efficacy evaluation. Using advanced statistical methods in the study design and analysis stages is therefore essential to immunogenicity risk assessment and mitigation strategies. Statistical Methods for Immunogenicity Assessment provides a single source of information on statistical concepts, principles, methods, and strategies for detection, quantification, assessment, and control of immunogenicity. The book first gives an overview of the impact of immunogenicity on biopharmaceutical development, regulatory requirements, and statistical methods and strategies used for immunogenicity detection, quantification, and risk assessment and mitigation. It then covers anti-drug antibody (ADA) assay development, optimization, validation, and transfer as well as the analysis of cut point, a key assay performance parameter in ADA assay development and validation. The authors illustrate how to apply statistical modeling approaches to establish associations between ADA and clinical outcomes, predict immunogenicity risk, and develop risk mitigation strategies. They also present various strategies for immunogenicity risk control. The book concludes with an explanation of the computer codes and algorithms of the statistical methods. A critical issue in the development of biologics, immunogenicity can cause early termination or limited use of the products if not managed well. This book shows how to use robust statistical methods for detecting, quantifying, assessing, and mitigating immunogenicity risk. It is an invaluable resource for anyone involved in immunogenicity risk assessment and control in both non-clinical and clinical biopharmaceutical development.

This book focuses on analytical similarity assessment in biosimilar product development following the FDA's recommended stepwise approach for obtaining totality-of-the-evidence for approval of biosimilar products. It covers concepts such as the tiered approach for assessment of similarity of critical quality attributes in the manufacturing process of biosimilar products, models/methods like the statistical model for classification of critical quality attributes, equivalence tests for critical quality attributes in Tier 1 and the corresponding sample size requirements, current issues, and recent developments in analytical similarity assessment.

Data Analysis with Competing Risks and Intermediate States explains when and how to use models and techniques for the analysis of competing risks and intermediate states. It covers the most recent insights on estimation techniques and discusses in detail how to interpret the obtained results. After introducing example studies from the biomedical and epidemiological fields, the book formally defines the concepts that play a role in analyses with competing risks and intermediate states. It addresses nonparametric estimation of the relevant quantities. The book then shows how to use a stacked data set that offers great flexibility in the modeling of covariable effects on the transition rates between states. It also describes three ways to quantify effects on the cumulative scale. Each chapter includes standard exercises that reflect on the concepts presented, a section on software that explains options in SAS and Stata and the functionality in the R program, and computer practicals that allow readers to practice with the techniques using an existing data set of bone marrow transplant patients. The book's website provides the R code for the computer practicals along with other material. For researchers with some experience in the analysis of standard time-to-event data, this practical and thorough treatment extends their knowledge and skills to the competing risks and multi-state settings. Researchers from other fields can also easily translate individuals and diseases to units and phenomena from their own areas.