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Consumer Health Informatics Enabling Digital Health for Everyone

Consumer Health Informatics Enabling Digital Health for Everyone

An engaging introduction to an exciting multidisciplinary field where positive impact depends less on technology than on understanding and responding to human motivations specific information needs and life constraints. Betsy L. Humphreys former Deputy Director National Library of Medicine This is a book for people who want to design or promote information technology that helps people be more active and informed participants in their healthcare. Topics include patient portals wearable devices apps websites smart homes and online communities focused on health. Consumer Healthcare Informatics: Enabling Digital Health for Everyone educates readers in the core concepts of consumer health informatics: participatory healthcare; health and e-health literacy; user-centered design; information retrieval and trusted information resources; and the ethical dimensions of health information and communication technologies. It presents the current state of knowledge and recent developments in the field of consumer health informatics. The discussions address tailoring information to key user groups including patients consumers caregivers parents children and young adults and older adults. For example apps are considered as not just a rich consumer technology with the promise of empowered personal data management and connectedness to community and healthcare providers but also a domain rife with concerns for effectiveness privacy and security requiring both designer and user to engage in critical thinking around their choices. This book’s unique contribution to the field is its focus on the consumer and patient in the context of their everyday life outside the clinical setting. Discussion of tools and technologies is grounded in this perspective and in a context of real-world use and its implications for design. There is an emphasis on empowerment through participatory and people-centered care. | Consumer Health Informatics Enabling Digital Health for Everyone

GBP 44.99
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Healthcare 4.0 Health Informatics and Precision Data Management

Healthcare 4.0 Health Informatics and Precision Data Management

The main aim of Healthcare 4. 0: Health Informatics and Precision Data Management is to improve the services given by the healthcare industry and to bring meaningful patient outcomes by applying the data information and knowledge in the healthcare domain. Features: Improves the quality of health data of a patient Presents a wide range of opportunities and renewed possibilities for healthcare systems Gives a way for carefully and meticulously tracking the provenance of medical records Accelerates the process of disease-oriented data and medical data arbitration Brings meaningful patient health outcomes Eradicates delayed clinical communications Helps the research intellectuals to step down further toward the disease and clinical data storage Creates more patient-centered services The precise focus of this handbook is on the potential applications and use of data informatics in healthcare including clinical trials tailored ailment data patient and ailment record characterization and health records management. | Healthcare 4. 0 Health Informatics and Precision Data Management

GBP 140.00
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Statistical Methods in Health Disparity Research

Statistical Methods in Health Disparity Research

A health disparity refers to a higher burden of illness injury disability or mortality experienced by one group relative to others attributable to multiple factors including socioeconomic status environmental factors insufficient access to health care individual risk factors and behaviors and inequalities in education. These disparities may be due to many factors including age income and race. Statistical Methods in Health Disparity Research will focus on their estimation ranging from classical approaches including the quantification of a disparity to more formal modeling to modern approaches involving more flexible computational approaches. Features: Presents an overview of methods and applications of health disparity estimation First book to synthesize research in this field in a unified statistical framework Covers classical approaches and builds to more modern computational techniques Includes many worked examples and case studies using real data Discusses available software for estimation The book is designed primarily for researchers and graduate students in biostatistics data science and computer science. It will also be useful to many quantitative modelers in genetics biology sociology and epidemiology.

GBP 90.00
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Absolute Risk Methods and Applications in Clinical Management and Public Health

Absolute Risk Methods and Applications in Clinical Management and Public Health

Absolute Risk: Methods and Applications in Clinical Management and Public Health provides theory and examples to demonstrate the importance of absolute risk in counseling patients devising public health strategies and clinical management. The book provides sufficient technical detail to allow statisticians epidemiologists and clinicians to build test and apply models of absolute risk. Features:Provides theoretical basis for modeling absolute risk including competing risks and cause-specific and cumulative incidence regression Discusses various sampling designs for estimating absolute risk and criteria to evaluate modelsProvides details on statistical inference for the various sampling designsDiscusses criteria for evaluating risk models and comparing risk models including both general criteria and problem-specific expected losses in well-defined clinical and public health applications Describes many applications encompassing both disease prevention and prognosis and ranging from counseling individual patients to clinical decision making to assessing the impact of risk-based public health strategies Discusses model updating family-based designs dynamic projections and other topicsRuth M. Pfeiffer is a mathematical statistician and Fellow of the American Statistical Association with interests in risk modeling dimension reduction and applications in epidemiology. She developed absolute risk models for breast cancer colon cancer melanoma and second primary thyroid cancer following a childhood cancer diagnosis. Mitchell H. Gail developed the widely used Gail model for projecting the absolute risk of invasive breast cancer. He is a medical statistician with interests in statistical methods and applications in epidemiology and molecular medicine. He is a member of the National Academy of Medicine and former President of the American Statistical Association. Both are Senior Investigators in the Division of Cancer Epidemiology and Genetics National Cancer Institute National Institutes of Health. | Absolute Risk Methods and Applications in Clinical Management and Public Health

GBP 46.99
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Introductory Statistics for the Health Sciences

Introductory Statistics for the Health Sciences

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.

GBP 44.99
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Statistics and Health Care Fraud How to Save Billions

Statistics and Health Care Fraud How to Save Billions

Statistics and Health Care Fraud: How to Save Billions helps the public to become more informed citizens through discussions of real world health care examples and fraud assessment applications. The author presents statistical and analytical methods used in health care fraud audits without requiring any mathematical background. The public suffers from health care overpayments either directly as patients or indirectly as taxpayers and fraud analytics provides ways to handle the large size and complexity of these claims. The book starts with a brief overview of global healthcare systems such as U. S. Medicare. This is followed by a discussion of medical overpayments and assessment initiatives using a variety of real world examples. The book covers subjects as: • Description and visualization of medical claims data • Prediction of fraudulent transactions • Detection of excessive billings • Revealing new fraud patterns • Challenges and opportunities with health care fraud analytics Dr. Tahir Ekin is the Brandon Dee Roberts Associate Professor of Quantitative Methods in McCoy College of Business Texas State University. His previous work experience includes a working as a statistician on health care fraud detection. His scholarly work on health care fraud has been published in a variety of academic journals including International Statistical Review The American Statistician and Applied Stochastic Models in Business and Industry. He is a recipient of the Texas State University 2018 Presidential Distinction Award in Scholar Activities and the ASA/NISS y-Bis 2016 Best Paper Awards. He has developed and taught courses in the areas of business statistics optimization data mining and analytics. Dr. Ekin also serves as Vice President of the International Society for Business and Industrial Statistics. | Statistics and Health Care Fraud How to Save Billions

GBP 24.99
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Analyzing Health Data in R for SAS Users

Analyzing Health Data in R for SAS Users

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 RDemonstrates descriptive statistics as well as linear and logistic regressionProvides exercise questions and answers at the end of each chapterUses examples from the publicly available dataset Behavioral Risk Factor Surveillance System (BRFSS) 2014 dataGuides the reader on producing a health analysis that could be published as a research reportGives an example of hypothesis-driven data analysisProvides examples of plots with a color insert

GBP 44.99
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Discrete Event Simulation for Health Technology Assessment

Discrete Event Simulation for Health Technology Assessment

Discover How to Apply DES to Problems Encountered in HTADiscrete event simulation (DES) has traditionally been used in the engineering and operations research fields. The use of DES to inform decisions about health technologies is still in its infancy. Written by specialists at the forefront of this area Discrete Event Simulation for Health Technology Assessment is the first book to make all the central concepts of DES relevant for health technology assessment (HTA). Accessible to beginners the book requires no prerequisites and describes the concepts with as little jargon as possible. The book first covers the essential concepts and their implementation. It next provides a fully worked out example using both a widely available spreadsheet program (Microsoft Excel) and a popular specialized simulation package (Arena). It then presents approaches to analyze the simulations including the treatment of uncertainty; tackles the development of the required equations; explains the techniques to verify that the models are as efficient as possible; and explores the indispensable topic of validation. The book also covers a variety of non-essential yet handy topics such as the animation of a simulation and extensions of DES and incorporates a real case study involving screening strategies for breast cancer surveillance. This book guides you in leveraging DES in your assessments of health technologies. After reading the chapters in sequence you will be able to construct a realistic model designed to help in the assessment of a new health technology.

GBP 44.99
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Statistical Analytics for Health Data Science with SAS and R

Statistical Analytics for Health Data Science with SAS and R

This book aims to compile typical fundamental-to-advanced statistical methods to be used for health data sciences. Although the book promotes applications to health and health-related data the models in the book can be used to analyze any kind of data. The data are analyzed with the commonly used statistical software of R/SAS (with online supplementary on SPSS/Stata). The data and computing programs will be available to facilitate readers’ learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for the most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models compilations of a variety of publicly available data and illustrations of data analytics using commonly used statistical software of SAS/R. We will have the data and computer programs available for readers to replicate and implement the new methods. The primary readers would be applied data scientists and practitioners in any field of data science applied statistical analysts and scientists in public health academic researchers and graduate students in statistics and biostatistics. The secondary readers would be R&D professionals/practitioners in industry and governmental agencies. This book can be used for both teaching and applied research. | Statistical Analytics for Health Data Science with SAS and R

GBP 74.99
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Advances in Mobile Health Technology A Research Perspective

Advances in Mobile Health Technology A Research Perspective

The COVID-19 pandemic upended the lives of many and taught us the critical importance of taking care of one’s health and wellness. Technological advances coupled with advances in healthcare has enabled the widespread growth of a new area called mobile health or mHealth that has completely revolutionized how people envision healthcare today. Just as smartphones and tablet computers are rapidly becoming the dominant consumer computer platforms mHealth technology is emerging as an integral part of consumer health and wellness management regimes. The aim of this book is to inform readers about the this relatively modern technology from its history and evolution to the current state-of-the-art research developments and the underlying challenges related to privacy and security issues. The book’s intended audience includes individuals interested in learning about mHealth and its contemporary applications from students to researchers and practitioners working in this field. Both undergraduate and graduate students enrolled in college-level healthcare courses will find this book to be an especially useful companion and will be able to discover and explore novel research directions that will further enrich the field. | Advances in Mobile Health Technology A Research Perspective

GBP 42.99
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Statistical Topics in Health Economics and Outcomes Research

Statistical Topics in Health Economics and Outcomes Research

With ever-rising healthcare costs evidence generation through Health Economics and Outcomes Research (HEOR) plays an increasingly important role in decision-making about the allocation of resources. Accordingly it is now customary for health technology assessment and reimbursement agencies to request for HEOR evidence in addition to data from clinical trials to inform decisions about patient access to new treatment options. While there is a great deal of literature on HEOR there is a need for a volume that presents a coherent and unified review of the major issues that arise in application especially from a statistical perspective. Statistical Topics in Health Economics and Outcomes Research fulfils that need by presenting an overview of the key analytical issues and best practice. Special attention is paid to key assumptions and other salient features of statistical methods customarily used in the area and appropriate and relatively comprehensive references are made to emerging trends. The content of the book is purposefully designed to be accessible to readers with basic quantitative backgrounds while providing an in-depth coverage of relatively complex statistical issues. The book will make a very useful reference for researchers in the pharmaceutical industry academia and research institutions involved with HEOR studies. The targeted readers may include statisticians data scientists epidemiologists outcomes researchers health economists and healthcare policy and decision-makers.

GBP 44.99
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Geospatial Health Data Modeling and Visualization with R-INLA and Shiny

Geospatial Health Data Modeling and Visualization with R-INLA and Shiny

Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden understand geographic and temporal patterns identify risk factors and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulating and transforming point areal and raster data Bayesian hierarchical models for disease mapping using areal and geostatistical data Fitting and interpreting spatial and spatio-temporal models with the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) approaches Creating interactive and static visualizations such as disease maps and time plots Reproducible R Markdown reports interactive dashboards and Shiny web applications that facilitate the communication of insights to collaborators and policymakers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia cancer in Scotland and USA and air pollution in Spain. Examples in the book focus on health applications but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology ecology demography or criminology. The book provides clear descriptions of the R code for data importing manipulation modelling and visualization as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students researchers and practitioners. | Geospatial Health Data Modeling and Visualization with R-INLA and Shiny

GBP 84.99
1

Statistical Testing Strategies in the Health Sciences

Statistical Testing Strategies in the Health Sciences

Statistical Testing Strategies in the Health Sciences provides a compendium of statistical approaches for decision making ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced empirical likelihood techniques. It bridges the gap between theoretical statistical methods and practical procedures applied to the planning and analysis of health-related experiments. The book is organized primarily based on the type of questions to be answered by inference procedures or according to the general type of mathematical derivation. It establishes the theoretical framework for each method with a substantial amount of chapter notes included for additional reference. It then focuses on the practical application for each concept providing real-world examples that can be easily implemented using corresponding statistical software code in R and SAS. The book also explains the basic elements and methods for constructing correct and powerful statistical decision-making processes to be adapted for complex statistical applications. With techniques spanning robust statistical methods to more computationally intensive approaches this book shows how to apply correct and efficient testing mechanisms to various problems encountered in medical and epidemiological studies including clinical trials. Theoretical statisticians medical researchers and other practitioners in epidemiology and clinical research will appreciate the book’s novel theoretical and applied results. The book is also suitable for graduate students in biostatistics epidemiology health-related sciences and areas pertaining to formal decision-making mechanisms.

GBP 44.99
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Monitoring the Health of Populations by Tracking Disease Outbreaks Saving Humanity from the Next Plague

Monitoring the Health of Populations by Tracking Disease Outbreaks Saving Humanity from the Next Plague

With COVID-19 sweeping across the globe with near impunity it is thwarting governments and health organizations efforts to contain it. Not since the 1918 Spanish Flu have citizens of developed countries experienced such a large-scale disease outbreak that is having devastating health and economic impacts. One reason such outbreaks are not more common has been the success of the public health community including epidemiologists and biostatisticians in identifying and then mitigating or eliminating the outbreaks. Monitoring the Health of Populations by Tracking Disease Outbreaks: Saving Humanity from the Next Plague is the story of the application of statistics for disease detection and tracking. The work of public health officials often crucially depends on statistical methods to help discern whether an outbreak may be occurring and if there is sufficient evidence of an outbreak then to locate and track it. Statisticians also help collect critical information and they analyze the resulting data to help investigators zero in on a cause for a disease. With the recent outbreaks of diseases such as swine and bird flu Ebola and now COVID-19 the role that epidemiologists and biostatisticians play is more important than ever. Features: · Discusses the crucial roles of statistics in early disease detection. · Outlines the concepts and methods of disease surveillance. · Covers surveillance techniques for communicable diseases like Zika and chronic diseases such as cancer. · Gives real world examples of disease investigations including smallpox syphilis anthrax yellow fever and microcephaly (and its relationship to the Zika virus). Via the process of identifying an outbreak finding its cause and developing a plan to prevent its reoccurrence this book tells the story of how medical and public health professionals use statistics to help mitigate the effects of disease. This book will help readers understand how statisticians and epidemiologists help combat the spread of such diseases in order to improve public health across the world. | Monitoring the Health of Populations by Tracking Disease Outbreaks Saving Humanity from the Next Plague

GBP 31.99
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Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

In engineering and quality control various situations including process validation and design verification require equivalence and noninferiority tests. Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers presents methods for using validation and verification test data to demonstrate equivalence and noninferiority in engineering and applied science. The book covers numerous tests drawn from the author’s more than 30 years of work in a range of industrial settings. It provides computational formulas for the tests methods to determine or justify sample sizes and formulas to calculate power and operating characteristic curves. The methods are accessible using standard statistical software and do not require complicated programming. The book also includes computer code and screen shots for SAS R and JMP. This book provides you with a guide to performing validation and verification tests that demonstrate the adequacy of your process system or product. It will help you choose the best test for your application.

GBP 59.99
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Handbook of Spatial Epidemiology

Handbook of Spatial Epidemiology

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.

GBP 66.99
1

Intelligent Systems in Healthcare and Disease Identification using Data Science

Intelligent Systems in Healthcare and Disease Identification using Data Science

The health technology has become a hot topic in academic research. It employs the theory of social networks into the different levels of the prediction and analysis and has brought new possibilities for the development of technology. This book is a descriptive summary of challenges and methods using disease identification with various case studies from diverse authors across the globe. One of the new buzzwords in healthcare sector that has become popular over years is health informatics. Healthcare professionals must deal with an increasing number of computers and computer programs in their daily work. With rapid growth of digital data the role of analytics in healthcare has created a significant impact on healthcare professional’s life. Improvements in storage data computational power and paral- lelization has also contributed to uptake this technology. This book is intended for use by researchers health informatics professionals academicians and undergraduate and postgraduate students interested in knowing more about health informatics. It aims to provide a brief overview about informatics its history and area of practice laws in health informatics challenges and technologies in health informatics applica- tion of informatics in various sectors and so on. Finally the research avenues in health informatics along with some case studies are discussed. | Intelligent Systems in Healthcare and Disease Identification using Data Science

GBP 84.99
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Nonparametric Statistical Tests A Computational Approach

Nonparametric Statistical Tests A Computational Approach

Nonparametric Statistical Tests: A Computational Approach describes classical nonparametric tests as well as novel and little-known methods such as the Baumgartner-Weiss-Schindler and the Cucconi tests. The book presents SAS and R programs allowing readers to carry out the different statistical methods such as permutation and bootstrap tests. The author considers example data sets in each chapter to illustrate methods. Numerous real-life data from various areas including the bible and their analyses provide for greatly diversified reading. The book covers: Nonparametric two-sample tests for the location-shift model specifically the Fisher-Pitman permutation test the Wilcoxon rank sum test and the Baumgartner-Weiss-Schindler test Permutation tests location-scale tests tests for the nonparametric Behrens-Fisher problem and tests for a difference in variability Tests for the general alternative including the (Kolmogorov-)Smirnov test ordered categorical and discrete numerical data Well-known one-sample tests such as the sign test and Wilcoxon’s signed rank test a modification suggested by Pratt (1959) a permutation test with original observations and a one-sample bootstrap test are presented. Tests for more than two groups the following tests are described in detail: the Kruskal-Wallis test the permutation F test the Jonckheere-Terpstra trend test tests for umbrella alternatives and the Friedman and Page tests for multiple dependent groups The concepts of independence and correlation and stratified tests such as the van Elteren test and combination tests The applicability of computer-intensive methods such as bootstrap and permutation tests for non-standard situations and complex designs Although the major development of nonparametric methods came to a certain end in the 1970s their importance undoubtedly persists. What is still needed is a computer assisted evaluation of their main properties. This book closes that gap. | Nonparametric Statistical Tests A Computational Approach

GBP 69.99
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Bayesian Disease Mapping Hierarchical Modeling in Spatial Epidemiology Third Edition

Bayesian Disease Mapping Hierarchical Modeling in Spatial Epidemiology Third Edition

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. | Bayesian Disease Mapping Hierarchical Modeling in Spatial Epidemiology Third Edition

GBP 46.99
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Disease Mapping From Foundations to Multidimensional Modeling

Disease Mapping From Foundations to Multidimensional Modeling

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. | Disease Mapping From Foundations to Multidimensional Modeling

GBP 51.99
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Real-World Evidence in a Patient-Centric Digital Era

Real-World Evidence in a Patient-Centric Digital Era

Real-world evidence is defined as evidence generated from real-world data outside randomized controlled trials. As scientific discoveries and methodologies continue to advance real-world data and their companion technologies offer powerful new tools for evidence generation. Real-World Evidence in a Patient-Centric Digital Era provides perspectives examples and insights on the innovative application of real-world evidence to meet patient needs and improve healthcare with a focus on the pharmaceutical industry. This book presents an overview of key analytical issues and best practices. Special attention is paid to the development methodologies and other salient features of the statistical and data science techniques that are customarily used to generate real-world evidence. It provides a review of key topics and emerging trends in cutting-edge data science and health innovation. Features: Provides an overview of statistical and analytic methodologies in real-world evidence to generate insights on healthcare with a special focus on the pharmaceutical industry Examines timely topics of high relevance to industry such as bioethical considerations regulatory standards and compliance requirements Highlights emerging and current trends and provides guidelines for best practices Illustrates methods through examples and use-case studies to demonstrate impact Provides guidance on software choices and digital applications for successful analytics Real-World Evidence in a Patient-Centric Digital Era will be a vital reference for medical researchers health technology innovators data scientists epidemiologists population health analysts health economists outcomes researchers policymakers and analysts in the healthcare industry.

GBP 99.99
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Data Science for Effective Healthcare Systems

Data Science for Effective Healthcare Systems

Data Science for Effective Healthcare Systems has a prime focus on the importance of data science in the healthcare domain. Various applications of data science in the health care domain have been studied to find possible solutions. In this period of COVID-19 pandemic data science and allied areas plays a vital role to deal with various aspect of health care. Image processing detection & prevention from COVID-19 virus drug discovery early prediction and prevention of diseases are some thrust areas where data science has proven to be indispensable. Key Features: The book offers comprehensive coverage of the most essential topics including: Big Data Analytics Applications & Challenges in Healthcare Descriptive Predictive and Prescriptive Analytics in Healthcare Artificial Intelligence Machine Learning Deep Learning and IoT in Healthcare Data Science in Covid-19 Diabetes Coronary Heart Diseases Breast Cancer Brain Tumor The aim of this book is also to provide the future scope of these technologies in the health care domain. Last but not the least this book will surely benefit research scholar persons associated with healthcare faculty research organizations and students to get insights into these emerging technologies in the healthcare domain.

GBP 110.00
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Economic Evaluation of Cancer Drugs Using Clinical Trial and Real-World Data

Economic Evaluation of Cancer Drugs Using Clinical Trial and Real-World Data

Cancer is a major healthcare burden across the world and impacts not only the people diagnosed with various cancers but also their families carers and healthcare systems. With advances in the diagnosis and treatment more people are diagnosed early and receive treatments for a disease where few treatments options were previously available. As a result the survival of patients with cancer has steadily improved and in most cases patients who are not cured may receive multiple lines of treatment often with financial consequences for the patients insurers and healthcare systems. Although many books exist that address economic evaluation Economic Evaluation of Cancer Drugs using Clinical Trial and Real World Data is the first unified text that specifically addresses the economic evaluation of cancer drugs. The authors discuss how to perform cost-effectiveness analyses while emphasising the strategic importance of designing cost-effectiveness into cancer trials and building robust economic evaluation models that have a higher chance of reimbursement if truly cost-effective. They cover the use of real-world data using cancer registries and discuss how such data can support or complement clinical trials with limited follow up. Lessons learned from failed reimbursement attempts factors predictive of successful reimbursement and the different payer requirements across major countries including US Australia Canada UK Germany France and Italy are also discussed. The book includes many detailed practical examples case studies and thought-provoking exercises for use in classroom and seminar discussions. Iftekhar Khan is a medical statistician and health economist and a lead statistician at Oxford Unviersity’s Center for Statistics in Medicine. Professor Khan is also a Senior Research Fellow in Health Economics at University of Warwick and is a Senior Statistical Assessor within the Licensing Division of the UK Medicine and Health Regulation Agency. Ralph Crott is a former professor in Pharmacoeconomics at the University of Montreal in Quebec Canada and former head of the EORTC Health Economics Unit and former senior health economist at the Belgian HTA organization. Zahid Bashir has over twelve years experience working in the pharmaceutical industry in medical affairs and oncology drug development where he is involved in the design and execution of oncology clinical trials and development of reimbursement dossiers for HTA submission. | Economic Evaluation of Cancer Drugs Using Clinical Trial and Real-World Data

GBP 44.99
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Bayesian Methods in Pharmaceutical Research

Bayesian Methods in Pharmaceutical Research

Since the early 2000s there has been increasing interest within the pharmaceutical industry in the application of Bayesian methods at various stages of the research development manufacturing and health economic evaluation of new health care interventions. In 2010 the first Applied Bayesian Biostatistics conference was held with the primary objective to stimulate the practical implementation of Bayesian statistics and to promote the added-value for accelerating the discovery and the delivery of new cures to patients. This book is a synthesis of the conferences and debates providing an overview of Bayesian methods applied to nearly all stages of research and development from early discovery to portfolio management. It highlights the value associated with sharing a vision with the regulatory authorities academia and pharmaceutical industry with a view to setting up a common strategy for the appropriate use of Bayesian statistics for the benefit of patients. The book covers:Theory methods applications and computingBayesian biostatistics for clinical innovative designsAdding value with Real World EvidenceOpportunities for rare orphan diseases and pediatric developmentApplied Bayesian biostatistics in manufacturingDecision making and Portfolio management Regulatory perspective and public health policies Statisticians and data scientists involved in the research development and approval of new cures will be inspired by the possible applications of Bayesian methods covered in the book. The methods applications and computational guidance will enable the reader to apply Bayesian methods in their own pharmaceutical research. | Bayesian Methods in Pharmaceutical Research

GBP 44.99
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Statistical Design Monitoring and Analysis of Clinical Trials Principles and Methods

Statistical Design Monitoring and Analysis of Clinical Trials Principles and Methods

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

GBP 82.99
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