There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime after diagnosis or treatment. In contrast, Dynamic Prediction in Clinical Survival Analysis focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models. Designed to be useful to applied statisticians and clinical epidemiologists, each chapter in the book has a practical focus on the issues of working with real life data. Chapters conclude with additional material either on the interpretation of the models, alternative models, or theoretical background. The book consists of four parts: * Part I deals with prognostic models for survival data using (clinical) information available at baseline, based on the Cox model * Part II is about prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated * Part III is dedicated to the use of time-dependent information in dynamic prediction * Part IV explores dynamic prediction models for survival data using genomic data Dynamic Prediction in Clinical Survival Analysis summarizes cutting-edge research on the dynamic use of predictive models with traditional and new approaches. Aimed at applied statisticians who actively analyze clinical data in collaboration with clinicians, the analyses of the different data sets throughout the book demonstrate how predictive models can be obtained from proper data sets.

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.

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.

This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis .

Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes.

The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis.

An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.

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.

This unique volume provides self-contained accounts of some recent trends in Biostatistics methodology and their applications. It includes state-of-the-art reviews and original contributions.

The articles included in this volume are based on a careful selection of peer-reviewed papers, authored by eminent experts in the field, representing a well balanced mix of researchers from the academia, R&D sectors of government and the pharmaceutical industry.

The book is also intended to give advanced graduate students and new researchers a scholarly overview of several research frontiers in biostatistics, which they can use to further advance the field through development of new techniques and results.

Within days of the first reports of patients suffering from a mysterious pneumonia in Wuhan, scientists in China had produced a complete genetic sequence of the virus and confirmed that it was a novel SARS-like coronavirus. The genetic sequence was deposited in a public database, making the genetic code available to scientists anywhere in the world. The result was that weeks before the WHO declared the outbreak a global public health emergency and months before COVID-19 was formally designated a pandemic, virologists around the world were already studying the protein spikes on the virus and designing vaccines, which were developed much more quickly, and turned out to be much more effective, than even the most optimistic had predicted. All of this was possible because a biological revolution had taken place a decade earlier: the world had moved into the postgenomic era. In this book, the distinguished microbiologist Hugh Pennington argues that COVID-19 is the first 'postgenomic pandemic' - that is, the first pandemic to sweep the world after the postgenomic era was initiated in 2008. Pennington explains the science behind this crucial development and shows how it has revolutionized our ways of understanding and dealing with pandemics, including the pandemic that brought our world to its knees.

This book gives a comprehensive survey of the epidemiology of common environmental exposures, including diet, water, particulates in outdoor air, nitrogen dioxide, ozone, environmental tobacco smoke, radon in homes, electromagnetic fields, and lead. Design and analysis issues, risk assessment and meta-analysis, and future directions in environmental epidemiology are also covered. All chapters provide a review of the literature as well as a discussion of important methodologic issues, particularly exposure assessment and statistical methods. The book's focus is on environmental exposures that are suspected of causing disease, but often remain controversial. Most of these exposures are widespread and low-level, and are thought to increase the risk of chronic diseases that have many causes. Although the disease risks due to these exposures may be low, the public health burden may be important because large numbers of people are exposed. Such exposures are inherently difficult to detect via observational epidemiology. This book seeks to clarify the challenges involved and suggests ways to move forward. It will be useful to students and practitioners of public health, environmental health, and epidemiology.

The 2009 influenza pandemic, like all emerging infections, had unique characteristics and challenges. This book examines the epidemiology, clinical manifestations and outcome of the 2009 pandemic as compared to seasonal influenza and previous pandemics in both developed and developing countries. Consideration is given to the effectiveness of pre-pandemic planning in mitigating the severity of the disease and what can be done differently to lessen the impact of the next pandemic. As such, the book is designed to provide insight about what can be done going forward to further impact the morbidity and mortality due to both seasonal and pandemic influenza and many of these lessons can be applied to other emerging infections.There are many lessons to be learned from the 2009 pandemic. This book not only describes what happened in the 2009 pandemic, but also what can be done to better prepare for the next pandemic. Issues discussed include what components of the pandemic planning were effective and which were not. Additionally, the book describes research studies and policy changes that: 1) are needed to better predict the occurrence and severity of a pandemic; 2) improve prevention and treatment modalities; and 3) enable better communication with the public about actions they can take to protect themselves, families and communities.

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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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 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.

Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from the basics of disease mapping to the most advanced topics in this field. A multidimensional framework is offered that makes possible the joint modeling of several risks patterns corresponding to combinations of several factors, including age group, time period, disease, etc. Although theory will be covered, the applied component will be equally as important with lots of practical examples offered. Features: Discusses the very latest developments on multivariate and multidimensional mapping. Gives a single state-of-the-art framework that unifies most of the previously proposed disease mapping approaches. Balances epidemiological and statistical points-of-view. Requires no previous knowledge of disease mapping. Includes practical sessions at the end of each chapter with WinBUGs/INLA and real world datasets. Supplies R code for the examples in the book so that they can be reproduced by the reader. About the Authors: Miguel A. Martinez Beneito has spent his whole career working as a statistician for public health services, first at the epidemiology unit of the Valencia (Spain) regional health administration and later as a researcher at the public health division of FISABIO, a regional bio-sanitary research center. He has been also the Bayesian Hierarchical Models professor for several seasons at the University of Valencia Biostatics Master. Paloma Botella Rocamora has spent most of her professional career in academia although she now works as a statistician for the epidemiology unit of the Valencia regional health administration. Most of her research has been devoted to developing and applying disease mapping models to real data, although her work as a statistician in an epidemiology unit makes her develop and apply statistical methods to health data, in general.

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)

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.

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

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.

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).

The field of molecular evolution has experienced explosive growth in recent years due to the rapid accumulation of genetic sequence data, continuous improvements to computer hardware and software, and the development of sophisticated analytical methods. The increasing availability of large genomic data sets requires powerful statistical methods to analyse and interpret them, generating both computational and conceptual challenges for the field.
Computational Molecular Evolution provides an up-to-date and comprehensive coverage of modern statistical and computational methods used in molecular evolutionary analysis, such as maximum likelihood and Bayesian statistics. Yang describes the models, methods and algorithms that are most useful for analysing the ever-increasing supply of molecular sequence data, with a view to furthering our understanding of the evolution of genes and genomes. The book emphasizes essential concepts rather than mathematical proofs. It includes detailed derivations and implementation details, as well as numerous illustrations, worked examples, and exercises. It will be of relevance and use to students and professional researchers (both empiricists and theoreticians) in the fields of molecular phylogenetics, evolutionary biology, population genetics, mathematics, statistics and computer science. Biologists who have used phylogenetic software programs to analyze their own data will find the book particularly rewarding, although it should appeal to anyone seeking an authoritative overview of this exciting area of computational biology.

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.

Models for repeated measurements will be of interest to research statisticians in agriculture, medicine, economics, and psychology, and to the many consulting statisticians who want an up-to-date expository account of this important topic. The second edition of this successful book has been completely revised and updated to take account of developments in the area over the last few years. This book is organized into four parts. In the first part, the general context of repeated measurements is presented. In the following three parts, a large number of concrete examples, including data tables, is presented to illustrate the models available. The book also provides a very extensive and updated bibliography of the repeated measurements literature.