Review of the First Edition: The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses, graphics, and simulations directly relating to meta-analysis... A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs. -Journal of Applied Statistics Statistical Meta-Analysis with R and Stata, Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions, which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What's New in the Second Edition: Adds Stata programs along with the R programs for meta-analysis Updates all the statistical meta-analyses with R/Stata programs Covers fixed-effects and random-effects MA, meta-regression, MA with rare-event, and MA-IPD vs MA-SS Adds five new chapters on multivariate MA, publication bias, missing data in MA, MA in evaluating diagnostic accuracy, and network MA Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health, medical research, governmental agencies, and the pharmaceutical industry.

Structural equation modeling (SEM) is a very general and flexible multivariate technique that allows relationships among variables to be examined. The roots of SEM are in the social sciences. In writing this textbook, the authors look to make SEM accessible to a wider audience of researchers across many disciplines, addressing issues unique to health and medicine. SEM is often used in practice to model and test hypothesized causal relationships among observed and latent (unobserved) variables, including in analysis across time and groups. It can be viewed as the merging of a conceptual model, path diagram, confirmatory factor analysis, and path analysis. In this textbook the authors also discuss techniques, such as mixture modeling, that expand the capacity of SEM using a combination of both continuous and categorical latent variables. Features: Basic, intermediate, and advanced SEM topics Detailed applications, particularly relevant for health and medical scientists Topics and examples that are pertinent to both new and experienced SEM researchers Substantive issues in health and medicine in the context of SEM Both methodological and applied examples Numerous figures and diagrams to illustrate the examples As SEM experts situated among clinicians and multidisciplinary researchers in medical settings, the authors provide a broad, current, on the ground understanding of the issues faced by clinical and health services researchers and decision scientists. This book gives health and medical researchers the tools to apply SEM approaches to study complex relationships between clinical measurements, individual and community-level characteristics, and patient-reported scales.

The past three decades have witnessed modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery Applied Biostatistical Principles and Concepts provides practical knowledge using biological and biochemical specimen/samples in order to understand health and disease processes at cellular, clinical, and population levels. Concepts and techniques provided will help researchers design and conduct studies, then translate data from bench to clinics in attempt to improve the health of patients and populations. This book is suitable for both clinicians and health or biological sciences students. It presents the reality in statistical modelling of health research data in a concise manner that will address the issue of "big data" type I error tolerance and probability value, effect size and confidence interval for precision, effect measure modification and interaction as well as confounders, thus allowing for more valid inferences and yielding results that are more reliable, valid and accurate.

Including the voices of key protagonists in the development of the public health workforce, this book is an important addition to the history of public health in England. It charts events leading to the unique achievement, from 2003, of specialist status, equivalent to public health medical consultants, for those from non-medical backgrounds. Setting these changes in context it discusses implications for practitioners and the wider UK public health workforce. A lively and comprehensive review of policy change, Multidisciplinary public health: Understanding the development of the modern workforce concludes with a reflection on the new public health system under way in England, making useful comparisons with the rest of the UK. This is an invaluable resource for anyone with an interest in public health, including public health academics and relevant postgraduate students.

Bayesian Analysis of Infectious Diseases -COVID-19 and Beyond shows how the Bayesian approach can be used to analyze the evolutionary behavior of infectious diseases, including the coronavirus pandemic. The book describes the foundation of Bayesian statistics while explicating the biology and evolutionary behavior of infectious diseases, including viral and bacterial manifestations of the contagion. The book discusses the application of Markov Chains to contagious diseases, previews data analysis models, the epidemic threshold theorem, and basic properties of the infection process. Also described are the chain binomial model for the evolution of epidemics. Features: Represents the first book on infectious disease from a Bayesian perspective. Employs WinBUGS and R to generate observations that follow the course of contagious maladies. Includes discussion of the coronavirus pandemic as well as many examples from the past, including the flu epidemic of 1918-1919. Compares standard non-Bayesian and Bayesian inferences. Offers the R and WinBUGS code on at www.routledge.com/9780367633868

Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient's individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest. Features: All you need to know to correctly make an online risk calculator from scratch Discrimination, calibration, and predictive performance with censored data and competing risks R-code and illustrative examples Interpretation of prediction performance via benchmarks Comparison and combination of rival modeling strategies via cross-validation Thomas A. Gerds is a professor at the Biostatistics Unit at the University of Copenhagen and is affiliated with the Danish Heart Foundation. He is the author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years. Michael W. Kattan is a highly cited author and Chair of the Department of Quantitative Health Sciences at Cleveland Clinic. He is a Fellow of the American Statistical Association and has received two awards from the Society for Medical Decision Making: the Eugene L. Saenger Award for Distinguished Service, and the John M. Eisenberg Award for Practical Application of Medical Decision-Making Research.

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.

Master GIS Applications on Modelling and Mapping the Risks of Diseases Infections transmitted by mosquitoes, ticks, triatomine bugs, sandflies, and black flies cause significant rates of death and disease, especially in developing countries. Why are certain places more susceptible to vector-borne diseases? Modelling Interactions Between Vector-Borne Diseases and Environment Using GIS reveals how using geographic information systems (GISs) can provide a greater understanding of how vector-borne diseases are spread and explores the use of geographical techniques in vector-borne disease monitoring, management, and control. This text provides readers with a better understanding of the vector-borne disease problem and its impact on public health. Introduces New Spatial Approaches Based on Location and Environment The book exposes readers to information on how to identify vector hotspots, determine when and where they can occur, and eliminate vector breeding sites. Utilizing simple illustrations based on real data, as well as the authors' more than 20 years of experience in the field, this text combines key spatial analysis techniques available in modern GIS with real-world applications. It offers step-by-step instruction on developing vector-borne disease risk models at different spatial and temporal scales and helps practitioners formulate disease causation hypotheses and identify areas at risk. In addition, it addresses medical geography, GIS, spatial analysis, and modelling, and covers other factors related to the spread of vector-borne diseases. This book: Gives an overview of common vector-borne diseases, GIS-based mapping and modelling, impacts of climate change on vector distributions, and availability and importance of accurate epidemiologically relevant spatial data Describes modelling and simulating the prevalence of vector-borne diseases around the world Summarizes some key spatial techniques and how they can be used to aid in the analysis of geographical and attributed data Defines the concept of establishing and characterizing spatial data systems, including their quality, errors, references, and issues of scale, and building such a system from often quite separate, disparate sources Shows how to develop weather-based predictive modelling, which can be used to predict the weekly trend of vector abundance Provides a GIS case study for modelling the future potential distribution of vector-borne disease based on different climatic change scenarios Modelling Interactions Between Vector-Borne Diseases and Environment Using GIS combines spatial analysis techniques available in modern GIS, together with real-world applications to provide you with a better understanding of ways to map, model, prevent, and control vector-borne diseases.

Dynamical Biostatistical Models presents statistical models and methods for the analysis of longitudinal data. The book focuses on models for analyzing repeated measures of quantitative and qualitative variables and events history, including survival and multistate models. Most of the advanced methods, such as multistate and joint models, can be applied using SAS or R software. The book describes advanced regression models that include the time dimension, such as mixed-effect models, survival models, multistate models, and joint models for repeated measures and time-to-event data. It also explores the possibility of unifying these models through a stochastic process point of view and introduces the dynamic approach to causal inference. Drawing on much of their own extensive research, the authors use three main examples throughout the text to illustrate epidemiological questions and methodological issues. Readers will see how each method is applied to real data and how to interpret the results.

Analyzing Longitudinal Clinical Trial Data: A Practical Guide provides practical and easy to implement approaches for bringing the latest theory on analysis of longitudinal clinical trial data into routine practice.The book, with its example-oriented approach that includes numerous SAS and R code fragments, is an essential resource for statisticians and graduate students specializing in medical research. The authors provide clear descriptions of the relevant statistical theory and illustrate practical considerations for modeling longitudinal data. Topics covered include choice of endpoint and statistical test; modeling means and the correlations between repeated measurements; accounting for covariates; modeling categorical data; model verification; methods for incomplete (missing) data that includes the latest developments in sensitivity analyses, along with approaches for and issues in choosing estimands; and means for preventing missing data. Each chapter stands alone in its coverage of a topic. The concluding chapters provide detailed advice on how to integrate these independent topics into an over-arching study development process and statistical analysis plan.

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 ethics and ideologies, inequalities, and changed social understandings 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 II: Social Consequences and Cultural Adaptations, 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 fields of rare diseases research and orphan products development continue to expand with more products in research and development status. In recent years, the role of the patient advocacy groups has evolved into a research partner with the academic research community and the bio-pharmaceutical industry. Unique approaches to research and development require epidemiological data not previously available to assist in protocol study design and patient recruitment for clinical trials required by regulatory agencies prior to approval for access by patents and practicing physicians.

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 ethics and ideologies, inequalities, and changed social understandings 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 II: Social Consequences and Cultural Adaptations, 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.

Provides a concise and intuitive overview of the role of statistics in surgical practice. Uses no mathematics but includes narrative descriptions of statistical concepts. Includes topics of specific interest to surgeons. Provides real surgical examples to illustrate applications.

Bayesian Approaches in Oncology Using R and OpenBUGS serves two audiences: those who are familiar with the theory and applications of bayesian approach and wish to learn or enhance their skills in R and OpenBUGS, and those who are enrolled in R and OpenBUGS-based course for bayesian approach implementation. For those who have never used R/OpenBUGS, the book begins with a self-contained introduction to R that lays the foundation for later chapters. Many books on the bayesian approach and the statistical analysis are advanced, and many are theoretical. While most of them do cover the objective, the fact remains that data analysis can not be performed without actually doing it, and this means using dedicated statistical software. There are several software packages, all with their specific objective. Finally, all packages are free to use, are versatile with problem-solving, and are interactive with R and OpenBUGS. This book continues to cover a range of techniques related to oncology that grow in statistical analysis. It intended to make a single source of information on Bayesian statistical methodology for oncology research to cover several dimensions of statistical analysis. The book explains data analysis using real examples and includes all the R and OpenBUGS codes necessary to reproduce the analyses. The idea is to overall extending the Bayesian approach in oncology practice. It presents four sections to the statistical application framework: Bayesian in Clinical Research and Sample Size Calcuation Bayesian in Time-to-Event Data Analysis Bayesian in Longitudinal Data Analysis Bayesian in Diagnostics Test Statistics This book is intended as a first course in bayesian biostatistics for oncology students. An oncologist can find useful guidance for implementing bayesian in research work. It serves as a practical guide and an excellent resource for learning the theory and practice of bayesian methods for the applied statistician, biostatistician, and data scientist.

There is an increasing need for educational resources for statisticians and investigators. Reflecting this, the goal of this book is to provide readers with a sound foundation in the statistical design, conduct, and analysis of clinical trials. Furthermore, it is intended as a guide for statisticians and investigators with minimal clinical trial experience who are interested in pursuing a career in this area. The advancement in genetic and molecular technologies have revolutionized drug development. In recent years, clinical trials have become increasingly sophisticated as they incorporate genomic studies, and efficient designs (such as basket and umbrella trials) have permeated the field. This book offers the requisite background and expert guidance for the innovative statistical design and analysis of clinical trials in oncology. Key Features: Cutting-edge topics with appropriate technical background Built around case studies which give the work a "hands-on" approach Real examples of flaws in previously reported clinical trials and how to avoid them Access to statistical code on the book's website Chapters written by internationally recognized statisticians from academia and pharmaceutical companies Carefully edited to ensure consistency in style, level, and approach Topics covered include innovating phase I and II designs, trials in immune-oncology and rare diseases, among many others

Through the framework of understanding health inequalities as a 'wicked problem' the book develops an applied approach to researching, understanding and addressing them by drawing on complexity theory. Case studies illuminate the text, illustrating and discussing the issues in real life terms and enabling public health, health promotion and health policy students to understand and address the complexities of health inequalities.

This book traces the growth in U.S. scientific and political interest in the eradication of cholera and describes the medical research and training facilities founded by the United States in Asia between 1947 and 1980.

This book demonstrates the importance and potential role of Traditional Ecological Knowledge in foreseeing and curbing future global pandemics. The reduction of species diversity has increased the risk of global pandemics and it is therefore not only imperative to articulate and disseminate knowledge on the linkages between human activities and the transmission of viruses to humans, but also to create policy pathways for operationalizing that knowledge to help solve future problems. Although this book has been prompted by the COVID-19 pandemic, it lays a policy foundation for the effective management or possible prevention of similar pandemics in the future. One effective way of establishing this linkage with a view to promoting planet health is by understanding the traditional ecological knowledge of indigenous peoples with a view to demonstrating the significant impact it has on keeping nature intact. This book argues for the deployment of traditional ecological knowledge for land use management in the preservation of biodiversity as a means for effectively managing the transmission of viruses from animals to humans and ensuring planetary health. The book is not projecting traditional ecological knowledge as a panacea to pandemics but rather accentuating its critical role in the effective mitigation of future pandemics. This book will be of great interest to students and scholars of traditional ecological knowledge, indigenous studies, animal ecology, environmental ethics and environmental studies more broadly.

With the critical role of statistics in the design, conduct, analysis and reporting of clinical trials or observational studies intended for regulatory purposes, numerous guidelines have been issued by regulatory authorities around the world focusing on statistical issues related to drug development. However, the available literature on this important topic is sporadic, and often not readily accessible to drug developers or regulatory personnel. This book provides a systematic exposition of the interplay between the two disciplines, including emerging themes pertaining to the acceleration of the development of pharmaceutical medicines to serve patients with unmet needs. Features: Regulatory and statistical interactions throughout the drug development continuum The critical role of the statistician in relation to the changing regulatory and healthcare landscapes Statistical issues that commonly arise in the course of drug development and regulatory interactions Trending topics in drug development, with emphasis on current regulatory thinking and the associated challenges and opportunities The book is designed to be accessible to readers with an intermediate knowledge of statistics, and can be a useful resource to statisticians, medical researchers, and regulatory personnel in drug development, as well as graduate students in the health sciences. The authors' decades of experience in the pharmaceutical industry and academia, and extensive regulatory experience, comes through in the many examples throughout the book.

Recognised as the most influential publication in the field, ARM facilitates deep understanding of the Rasch model and its practical applications. The authors review the crucial properties of the model and demonstrate its use with examples across the human sciences.

Readers will be able to understand and critically evaluate Rasch measurement research, perform their own Rasch analyses and interpret their results. The glossary and illustrations support that understanding, and the accessible approach means that it is ideal for readers without a mathematical background.

Highlights of the new edition include:

• More learning tools to strengthen readers’ understanding including chapter introductions, boldfaced key terms, chapter summaries, activities and suggested readings.
• Greater emphasis on the use of R packages; readers can download the R code from the Routledge website.
• Explores the distinction between numerical values, quantity and units, to understand the measurement and the role of the Rasch logit scale (Chapter 4).
• A new four-option data set from the IASQ (Instrumental Attitude towards Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (Chapter 6).
• Clarifies the relationship between Rasch measurement, path analysis and SEM, with a host of new examples of Rasch measurement applied across health sciences, education and psychology (Chapter 10).

Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business, and other social and health sciences. Professionals in these areas will also appreciate the book’s accessible introduction.

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

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

Emerging diseases are a major threat to modern societies, impacting individual welfare as well as economic development. The trend of newly emerging diseases has accelerated in the last two decades to such an extent that a new emerging infection is described at least once a year. The majority of such threats to modern society have been due to emergent viruses. This series of lecture notes provides grounding in understanding the drivers of disease emergence, the molecular processes which allow for virus diversity, the response of the host and environmental factors responsible for changing the balance between host and pathogen. Groups of viruses are described, each selected to illustrate certain features of disease emergence. These examples best illustrate how from past experience we may best be able to predict future outbreaks of novel diseases. Expecting the unexpected is a major challenge for health care personnel and public health officials alike, and the stakes have never been higher. As such, this book provides a timely overview of how best to prepare for disease emergence as it intends to increase awareness of how vulnerable modern society is in preparedness for such events.