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Bayesian Disease Mapping

Bayesian Disease Mapping Author Andrew B. Lawson
ISBN-10 9781351271745
Release 2018-05-20
Pages 464
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Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. In addition to the new material, the book also covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. The target audience for this text is public health specialists, epidemiologists, and biostatisticians who need to work with geo-referenced health data.



Applied Spatial Statistics for Public Health Data

Applied Spatial Statistics for Public Health Data Author Lance A. Waller
ISBN-10 0471662674
Release 2004-07-29
Pages 520
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An application-based introduction to the statistical analysis of spatially referenced health data Sparked by the growing interest in statistical methods for the analysis of spatially referenced data in the field of public health, Applied Spatial Statistics for Public Health Data fills the need for an introductory, application-oriented text on this timely subject. Written for practicing public health researchers as well as graduate students in related fields, the text provides a thorough introduction to basic concepts and methods in applied spatial statistics as well as a detailed treatment of some of the more recent methods in spatial statistics useful for public health studies that have not been previously covered elsewhere. Assuming minimal knowledge of spatial statistics, the authors provide important statistical approaches for assessing such questions as: Are newly occurring cases of a disease "clustered" in space? Do the cases cluster around suspected sources of increased risk, such as toxic waste sites or other environmental hazards? How do we take monitored pollution concentrations measured at specific locations and interpolate them to locations where no measurements were taken? How do we quantify associations between local disease rates and local exposures? After reviewing traditional statistical methods used in public health research, the text provides an overview of the basic features of spatial data, illustrates various geographic mapping and visualization tools, and describes the sources of publicly available spatial data that might be useful in public health applications.



Handbook of Spatial Epidemiology

Handbook of Spatial Epidemiology Author Andrew B. Lawson
ISBN-10 9781482253023
Release 2016-04-06
Pages 686
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Handbook of Spatial Epidemiology explains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists, geographers, and statisticians share interdisciplinary viewpoints on analyzing spatial data and space–time variations in disease incidences. These analyses can provide important information that leads to better decision making in public health. The first part of the book addresses general issues related to epidemiology, GIS, environmental studies, clustering, and ecological analysis. The second part presents basic statistical methods used in spatial epidemiology, including fundamental likelihood principles, Bayesian methods, and testing and nonparametric approaches. With a focus on special methods, the third part describes geostatistical models, splines, quantile regression, focused clustering, mixtures, multivariate methods, and much more. The final part examines special problems and application areas, such as residential history analysis, segregation, health services research, health surveys, infectious disease, veterinary topics, and health surveillance and clustering. Spatial epidemiology, also known as disease mapping, studies the geographical or spatial distribution of health outcomes. This handbook offers a wide-ranging overview of state-of-the-art approaches to determine the relationships between health and various risk factors, empowering researchers and policy makers to tackle public health problems.



Bayesian Regression Modeling with INLA

Bayesian Regression Modeling with INLA Author Xiaofeng Wang
ISBN-10 9781351165747
Release 2018-01-29
Pages 312
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This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through integrated nested Laplace approximations (INLA) and real data analysis using R. The INLA method directly computes very accurate approximations to the posterior marginal distributions and is a promising alternative to Markov chain Monte Carlo (MCMC) algorithms, which come with a range of issues that impede practical use of Bayesian models.



Design and Analysis of Quality of Life Studies in Clinical Trials

Design and Analysis of Quality of Life Studies in Clinical Trials Author Diane L. Fairclough
ISBN-10 1584882638
Release 2002-03-28
Pages 328
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More and more frequently, clinical trials include the evaluation of Health-Related Quality of Life (HRQoL), yet many investigators remain unaware of the unique measurement and analysis issues associated with the assessment of HRQoL. At the end of a study, clinicians and statisticians often face challenging and sometimes insurmountable analytic problems. Design and Analysis of Quality of Life Studies in Clinical Trials details these issues and presents a range of solutions. Written from the author's extensive experience in the field, it focuses on the very specific features of QoL data: its longitudinal nature, multidimensionality, and the problem of missing data. The author uses three real clinical trials throughout her discussions to illustrate practical implementation of the strategies and analytic methods presented. As Quality of Life becomes an increasingly important aspect of clinical trials, it becomes essential for clinicians, statisticians, and designers of these studies to understand and meet the challenges this kind of data present. In this book, SAS and S-PLUS programs, checklists, numerous figures, and a clear, concise presentation combine to provide readers with the tools and skills they need to successfully design, conduct, analyze, and report their own studies.



Spatio Temporal Methods in Environmental Epidemiology

Spatio Temporal Methods in Environmental Epidemiology Author Gavin Shaddick
ISBN-10 9781482237047
Release 2015-06-17
Pages 395
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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.



The R Book

The R Book Author Michael J. Crawley
ISBN-10 9781118448960
Release 2012-11-07
Pages 1080
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Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)



Spatial Complexity Informatics and Wildlife Conservation

Spatial Complexity  Informatics  and Wildlife Conservation Author Samuel A. Cushman
ISBN-10 4431877711
Release 2009-12-21
Pages 458
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As Earth faces the greatest mass extinction in 65 million years, the present is a moment of tremendous foment and emergence in ecological science. With leaps in advances in ecological research and the technical tools available, scientists face the critical task of challenging policymakers and the public to recognize the urgency of our global crisis. This book focuses directly on the interplay between theory, data, and analytical methodology in the rapidly evolving fields of animal ecology, conservation, and management. The mixture of topics of particular current relevance includes landscape ecology, remote sensing, spatial modeling, geostatistics, genomics, and ecological informatics. The greatest interest to the practicing scientist and graduate student will be the synthesis and integration of these topics to provide a composite view of the emerging field of spatial ecological informatics and its applications in research and management.



An Introduction to Mathematical Epidemiology

An Introduction to Mathematical Epidemiology Author Maia Martcheva
ISBN-10 9781489976123
Release 2015-10-20
Pages 453
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The book is a comprehensive, self-contained introduction to the mathematical modeling and analysis of infectious diseases. It includes model building, fitting to data, local and global analysis techniques. Various types of deterministic dynamical models are considered: ordinary differential equation models, delay-differential equation models, difference equation models, age-structured PDE models and diffusion models. It includes various techniques for the computation of the basic reproduction number as well as approaches to the epidemiological interpretation of the reproduction number. MATLAB code is included to facilitate the data fitting and the simulation with age-structured models.



Bayesian Analysis for Population Ecology

Bayesian Analysis for Population Ecology Author Ruth King
ISBN-10 1439811881
Release 2009-10-30
Pages 456
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Novel Statistical Tools for Conserving and Managing Populations By gathering information on key demographic parameters, scientists can often predict how populations will develop in the future and relate these parameters to external influences, such as global warming. Because of their ability to easily incorporate random effects, fit state-space models, evaluate posterior model probabilities, and deal with missing data, modern Bayesian methods have become important in this area of statistical inference and forecasting. Emphasising model choice and model averaging, Bayesian Analysis for Population Ecology presents up-to-date methods for analysing complex ecological data. Leaders in the statistical ecology field, the authors apply the theory to a wide range of actual case studies and illustrate the methods using WinBUGS and R. The computer programs and full details of the data sets are available on the book’s website. The first part of the book focuses on models and their corresponding likelihood functions. The authors examine classical methods of inference for estimating model parameters, including maximum-likelihood estimates of parameters using numerical optimisation algorithms. After building this foundation, the authors develop the Bayesian approach for fitting models to data. They also compare Bayesian and traditional approaches to model fitting and inference. Exploring challenging problems in population ecology, this book shows how to use the latest Bayesian methods to analyse data. It enables readers to apply the methods to their own problems with confidence.



Beginner s guide to spatial temporal and spatial temporal ecological data analysis with R INLA

Beginner s guide to spatial  temporal and spatial temporal ecological data analysis with R INLA Author Alain F. Zuur
ISBN-10 0957174195
Release 2017
Pages 362
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Beginner s guide to spatial temporal and spatial temporal ecological data analysis with R INLA has been writing in one form or another for most of life. You can find so many inspiration from Beginner s guide to spatial temporal and spatial temporal ecological data analysis with R INLA also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Beginner s guide to spatial temporal and spatial temporal ecological data analysis with R INLA book for free.



Handbook of Statistical Systems Biology

Handbook of Statistical Systems Biology Author Michael Stumpf
ISBN-10 9781119952046
Release 2011-09-09
Pages 530
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Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book: Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts. This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.



Ecological Inference

Ecological Inference Author Gary King
ISBN-10 0521542804
Release 2004-09-13
Pages 421
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Drawing upon the explosion of research in the field, a diverse group of scholars surveys strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays, first published in 2004, offers many important contributions to the study of ecological inference.



Measurement Error

Measurement Error Author John P. Buonaccorsi
ISBN-10 1420066587
Release 2010-03-02
Pages 464
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Over the last 20 years, comprehensive strategies for treating measurement error in complex models and accounting for the use of extra data to estimate measurement error parameters have emerged. Focusing on both established and novel approaches, Measurement Error: Models, Methods, and Applications provides an overview of the main techniques and illustrates their application in various models. It describes the impacts of measurement errors on naive analyses that ignore them and presents ways to correct for them across a variety of statistical models, from simple one-sample problems to regression models to more complex mixed and time series models. The book covers correction methods based on known measurement error parameters, replication, internal or external validation data, and, for some models, instrumental variables. It emphasizes the use of several relatively simple methods, moment corrections, regression calibration, simulation extrapolation (SIMEX), modified estimating equation methods, and likelihood techniques. The author uses SAS-IML and Stata to implement many of the techniques in the examples. Accessible to a broad audience, this book explains how to model measurement error, the effects of ignoring it, and how to correct for it. More applied than most books on measurement error, it describes basic models and methods, their uses in a range of application areas, and the associated terminology.



Statistical Methods in Healthcare

Statistical Methods in Healthcare Author Frederick Faltin
ISBN-10 9781119942047
Release 2012-07-24
Pages 520
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In recent years the number of innovative medicinal products and devices submitted and approved by regulatory bodies has declined dramatically. The medical product development process is no longer able to keep pace with increasing technologies, science and innovations and the goal is to develop new scientific and technical tools and to make product development processes more efficient and effective. Statistical Methods in Healthcare focuses on the application of statistical methodologies to evaluate promising alternatives and to optimize the performance and demonstrate the effectiveness of those that warrant pursuit is critical to success. Statistical methods used in planning, delivering and monitoring health care, as well as selected statistical aspects of the development and/or production of pharmaceuticals and medical devices are also addressed. With a focus on finding solutions to these challenges, this book: Provides a comprehensive, in-depth treatment of statistical methods in healthcare, along with a reference source for practitioners and specialists in health care and drug development. Offers a broad coverage of standards and established methods through leading edge techniques. Uses an integrated, case-study based approach, with focus on applications. Looks at the use of analytical and monitoring schemes to evaluate therapeutic performance. Features the application of modern quality management systems to clinical practice, and to pharmaceutical development and production processes. Addresses the use of modern Statistical methods such as Adaptive Design, Seamless Design, Data Mining, Bayesian networks and Bootstrapping that can be applied to support the challenging new vision. Practitioners in healthcare-related professions, ranging from clinical trials to care delivery to medical device design, as well as statistical researchers in the field, will benefit from this book.



Monitoring and Modeling of Global Changes A Geomatics Perspective

Monitoring and Modeling of Global Changes  A Geomatics Perspective Author Jonathan Li
ISBN-10 9789401798136
Release 2015-07-28
Pages 320
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The chapters in this book present state-of-the-art geomatics technologies applied in global environmental studies. This text provides the latest research findings and delivers complete references to related publications. This book will motivate the undergraduate and graduate students, researchers and practitioners to better understand the environmental changes with informed solutions. Global Change studies are increasingly considered a vital source of information to understand the Earth Environment, especially in the framework of human-induced, climate change and land use transformation. Satellite Earth Observing systems and geomatics technologies provide a unique tool to monitor and model the changes, respectively. While the range of applications and innovative techniques are always increasing, this book provides a summary of key study cases where satellite data offers critical information to understand the usefulness of the geomatics technologies and global environmental issues. Geomatics technologies provide powerful tools to model and analyze the effects of those global environmental changes towards minimizing their adverse impacts on human health and the environment.



Statistical Detection and Surveillance of Geographic Clusters

Statistical Detection and Surveillance of Geographic Clusters Author Peter Rogerson
ISBN-10 1584889365
Release 2008-12-24
Pages 324
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The widespread popularity of geographic information systems (GIS) has led to new insights in countless areas of application. It has facilitated not only the collection and storage of geographic data, but also the display of such data. Building on this progress by using an integrated approach, Statistical Detection and Monitoring of Geographic Clusters provides the statistical tools to identify whether data on a given map deviates significantly from expectations and to determine quickly whether new point patterns are emerging over time. The book begins with a review of statistical methods for cluster detection, organized according to the different types of hypotheses and questions about clustering that can be investigated. It then delineates methods that allow for the quick detection of emergent geographic clusters. The book delivers a cohesive presentation unlike that of most edited volumes. Drawing on the authors' extensive work in the field, the book delineates methods in such a way that they can be applied, almost instantly, to an array of disciplines. The readily applicable methods the book describes are useful for a multitude of problems in a variety of fields, particularly disease surveillance in the public health industry. Statistical Detection and Monitoring of Geographic Clusters is an essential volume for your reference shelf.