Epidemiology and Medical statistics Books

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Book SynopsisWritten in an informal and easy-going style suitable for a wide range of readers, this textbook uses real-world examples to teach the institutional details of healthcare and health insurance and explain the economics that underpin the behaviour of key players in these markets. A complete one-stop-shop for learning about the economics of healthcare.Trade Review'This is the book health economics instructors and students have been waiting for! The real-world examples immediately draw in readers and provide a foundation for looking at important issues in health care from an economic perspective. Dr. Friedson's approach to examining complex topics is both intuitive and hilarious.' Elizabeth Munnich, University of Louisville'A captivating tour of the economic foundations of modern healthcare. With practical examples and delightful humor, this book effortlessly breaks down complex concepts, making health economics accessible to diverse readers. A must-have resource for students seeking a comprehensive grasp of healthcare markets and the challenges they entail.' Sarah Miller, University of Michigan'This is a book on the economics of healthcare that is well organized, comprehensive in its coverage, and actually fun to read! It is well suited for a diverse audience, including undergraduate and graduate students from different degree programs, practitioners, and policy makers.' James H. Marton, Georgia State University'The hallmark of this book is its dedication to practicality. Demanding no prerequisite economics background, Friedson's 'Health Economics' is ideal for students and professionals in public health and healthcare management. Rich with insights into the economics of the healthcare industry, it occupies a unique space in the health economics literature.' Natallia Grey, Iowa State UniversityTable of ContentsPreface; Acknowledgments; Part I. Patients: 1. How Economists View Human Behavior; 2. Where Does Health Come From?; 3. Demand for Medical Care; 4. Health Insurance and Demand for Medical Care; 5. Evaluating Evidence; Part II. Providers: 6. Provider Firms; 7. The Medical Labor Force; 8. Providers and Incentives; 9. Hospitals; Part III. Health Insurers: 10. Health Insurance as a Product; 11. Adverse Selection; 12. Prices; 13. Managed Care; 14. Public Insurance; Part IV. The Sector at Large; 15. Pharmaceuticals; 16. Externalities; 17. Medical Malpractice; 18. Inequality; 19. International Comparisons.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisA New York Times bestsellerThe definitive account of the infectious diseases threatening humanity by Pulitzer Prizewinning investigative journalist Laurie GarrettProdigiously researched . . . A frightening vision of the future and a deeply unsettling one. Michiko Kakutani, The New York TimesAfter decades spent assuming that the conquest of infectious disease was imminent, people on all continents now find themselves besieged by AIDS, drug-resistant tuberculosis, cholera that defies chlorine water treatment, and exotic viruses that can kill in a matter of hours. Relying on extensive interviews with leading experts in virology, molecular biology, disease ecology, and medicine, as well as field research in sub-Saharan Africa, Western Europe, Central America, and the United States, Laurie Garrett''s The Coming Plague takes readers from the savannas of eastern Bolivia to the rain forests of the

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Book Synopsisto Logistic Regression.- Important Special Cases of the Logistic Model.- Computing the Odds Ratio in Logistic Regression.- Maximum Likelihood Techniques: An Overview.- Statistical Inferences Using Maximum Likelihood Techniques.- Modeling Strategy Guidelines.- Modeling Strategy for Assessing Interaction and Confounding.- Additional Modeling Strategy Issues.- Assessing Goodness of Fit for Logistic Regression.- Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves.- Analysis of Matched Data Using Logistic Regression.- Polytomous Logistic Regression.- Ordinal Logistic Regression.- Logistic Regression for Correlated Data: GEE.- GEE Examples.- Other Approaches for Analysis of Correlated Data.Trade ReviewFrom the reviews of the third edition:“The third edition of this book continues the tradition of the authors of a two-column book that really does act as a self-learning text. The left-hand column is like a collection of PowerPoint slides, including generic-style computer output and diagrams to visualize the relationship between concepts. Each chapter contains about 10 exercises, some routine calculation and some asking for explanation of particular points. Answers are provided immediately. … The reference list includes about 40 items and has been updated to include publications up to 2008.” (Alice Richardson, International Statistical Review, Vol. 79 (2), 2011)Table of Contentsto Logistic Regression.- Important Special Cases of the Logistic Model.- Computing the Odds Ratio in Logistic Regression.- Maximum Likelihood Techniques: An Overview.- Statistical Inferences Using Maximum Likelihood Techniques.- Modeling Strategy Guidelines.- Modeling Strategy for Assessing Interaction and Confounding.- Additional Modeling Strategy Issues.- Assessing Goodness of Fit for Logistic Regression.- Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves.- Analysis of Matched Data Using Logistic Regression.- Polytomous Logistic Regression.- Ordinal Logistic Regression.- Logistic Regression for Correlated Data: GEE.- GEE Examples.- Other Approaches for Analysis of Correlated Data.

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Book SynopsisThe vector-borne Zika virus joins avian influenza, Ebola, and yellow fever as recent public health crises threatening pandemicity.By a combination of stochastic modeling and economic geography, this book proposes two key causes together explain the explosive spread of the worst of the vector-borne outbreaks.Ecosystems in which such pathogens are largely controlled by environmental stochasticity are being drastically streamlined by both agribusiness-led deforestation and deficits in public health and environmental sanitation.Consequently, a subset of infections that once burned out relatively quickly in local forests are now propagating across susceptible human populations whose vulnerability to infection is often exacerbated in structurally adjusted cities. The resulting outbreaks are characterized by greater global extent, duration, and momentum.As infectious diseases in an age of nation states and global health programs cannot, as much of the present modeling literature presumes, be described by interacting populations of host, vector, and pathogen alone, a series of control theory models is also introduced here. These models, useful to researchers and health officials alike, explicitly address interactions between government ministries and the pathogens they aim to control.Table of ContentsThe Social Context of the Emergence of Vector-Borne Disease.- Modeling Vector-Borne Diseases in a Commoditized Landscape.- Modeling State Interventions.- Implications for Disease Intervention and Modeling.- Mathematical Appendix.- References.

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Book Synopsis​This unique textbook presents the field of modern epidemiology as a whole; it does not restrict itself to particular aspects. It stresses the fundamental ideas and their role in any situation of epidemiologic practice. Its structure is largely determined by didactic viewpoints.Epidemiology is the art of defining and investigating the influence of factors on the health of populations. Hence the book starts by sketching the role of epidemiology in public health. It then treats the epidemiology of many particular diseases; mathematical modelling of epidemics and immunity; health information systems; statistical methods and sample surveys; clinical epidemiology including clinical trials; nutritional, environmental, social, and genetic epidemiology; and the habitual tools of epidemiologic studies. The book also reexamines the basic difference between the epidemiology of infectious diseases and that of non-infectious ones.The organization of the topics by didactic aspects makes the book ideal for teaching. All examples and case studies are situated in a single country, namely Vietnam; this provides a particularly vivid picture of the role of epidemiology in shaping the health of a population. It can easily be adapted to other developing or transitioning countries.This volume is well suited for courses on epidemiology and public health at the upper undergraduate and graduate levels, while its specific examples make it appropriate for those who teach these fields in developing or emerging countries. New to this edition, in addition to minor revisions of almost all chapters:• Updated data about infectious and non-infectious diseases• An expanded discussion of genetic epidemiology• A new chapter, based on recent research of the authors, on how to build a coherent system of Public Health by using the insights provided by this volume. Trade Review“It is best suited for physicians who will likely not need to perform large epidemiological studies. … It is most suitable as an introductory book, used in combination with a more in-depth textbook or practical experience.” (Allison Dykstra, Doody's Book Reviews, November 22, 2019)Table of Contents

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Book SynopsisThis textbook focuses on the nascent field of Immunoepidemiology that addresses how differences in immune responses among individuals affect the epidemiology of infectious diseases, cancer, hypersensitivity, and autoimmunity. The idea for the book originated from a course entitled “Immunology for Epidemiologists“ at the Yale School of Public Health. While many fine textbooks are available that address the immunological responses of individuals to pathogens, these provided very little information regarding how immunological variation among populations affects the epidemiology of disease. And yet, it has long been recognized that there is great immunologic diversity among people, which can have a profound effect on the epidemiology of disease. Careful review of the immunologic and epidemiologic literature revealed that there have been relatively few publications concerning immunoepidemiology and that no textbook is available on the subject. This textbook therefore aims to fill this void by providing a much-needed tool to comprehensively and efficiently teach immunoepidemiology. The book includes a section on the basic principles of immunology, and then applies them to particular examples of disease in human populations. The target audience for this text book are Masters of Public Health students. Others who should also find it of interest include PhD students in epidemiology, immunology, medical students, generalists, and specialists in immunology, infectious diseases, cancer, and rheumatology.Table of ContentsSection I. Introduction​Chapter 1. Introduction to Immunology, Epidemiology, and ImmunoepidemiologyLinda Niccolai, Nancy H. Ruddle, and Peter J. KrauseChapter 2. Organization and Cells of the Immune SystemPaula B. Kavathas, PhD, Peter J. Krause, MD, Nancy H. Ruddle, PhDChapter 3. Innate Immunity: Recognition and Effector FunctionsPaula B. Kavathas, PhD, Peter J. Krause, MD, Nancy H. Ruddle, PhDChapter 4. Adaptive Immunity: Antigen Recognition by T and B LymphocytesPaula B. Kavathas, PhD, Peter J. Krause, MD, Nancy H. Ruddle, PhDChapter 5 Adaptive Immunity: Effector Functions, Regulation, and VaccinationPaula B. Kavathas, PhD, Peter J. Krause, MD, Nancy H. Ruddle, PhDChapter 6. Disorders of the Immune SystemPaula B. Kavathas, PhD, Peter J. Krause, MD, Nancy H. Ruddle, PhDChapter 7. Polymorphisms of Selected Components of the Innate and Adaptive Immune SystemsNancy H. Ruddle and Paula B. KavathasChapter 8. Immunoepidemiology of Immune DysfunctionEric Meffre, Peter J. Krause and Nancy H. RuddleChapter 9. Immunoepidemiology of Mycobacterium tuberculosisCamila D. Odio, Richard J. BucalaChapter 10. Host Immunity to Human immunodeficiency Virus InfectionElijah PaintsilChapter 11. Fungal ImmunoepidemiologyMarwan M. AzarChapter 12. Immunoepidemiology of Plasmodium falciparum MalariaAmy K. Bei, Sunil ParikhChapter 13. Immunoepidemiology of CancerXiaomei Ma, Rong WangChapter 14. Modeling Approaches Toward Understanding Infectious Disease TransmissionLaura A. Skrip, Jeffrey P. TownsendChapter 15. VaccinesDiane McMahon-PrattChapter 16. Immunotherapy for Infectious Diseases, Cancer, and AutoimmunityPeter J. Krause, Paula B. Kavathas, Nancy H. RuddleAppendixJane O'Bryan, Kara Fikrig, Peter J. Krause, Kate Nyhan

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Book SynopsisThis textbook focuses on the nascent field of Immunoepidemiology that addresses how differences in immune responses among individuals affect the epidemiology of infectious diseases, cancer, hypersensitivity, and autoimmunity. The idea for the book originated from a course entitled “Immunology for Epidemiologists“ at the Yale School of Public Health. While many fine textbooks are available that address the immunological responses of individuals to pathogens, these provided very little information regarding how immunological variation among populations affects the epidemiology of disease. And yet, it has long been recognized that there is great immunologic diversity among people, which can have a profound effect on the epidemiology of disease. Careful review of the immunologic and epidemiologic literature revealed that there have been relatively few publications concerning immunoepidemiology and that no textbook is available on the subject. This textbook therefore aims to fill this void by providing a much-needed tool to comprehensively and efficiently teach immunoepidemiology. The book includes a section on the basic principles of immunology, and then applies them to particular examples of disease in human populations. The target audience for this text book are Masters of Public Health students. Others who should also find it of interest include PhD students in epidemiology, immunology, medical students, generalists, and specialists in immunology, infectious diseases, cancer, and rheumatology.Table of ContentsSection I. Introduction​Chapter 1. Introduction to Immunology, Epidemiology, and ImmunoepidemiologyLinda Niccolai, Nancy H. Ruddle, and Peter J. KrauseChapter 2. Organization and Cells of the Immune SystemPaula B. Kavathas, PhD, Peter J. Krause, MD, Nancy H. Ruddle, PhDChapter 3. Innate Immunity: Recognition and Effector FunctionsPaula B. Kavathas, PhD, Peter J. Krause, MD, Nancy H. Ruddle, PhDChapter 4. Adaptive Immunity: Antigen Recognition by T and B LymphocytesPaula B. Kavathas, PhD, Peter J. Krause, MD, Nancy H. Ruddle, PhDChapter 5 Adaptive Immunity: Effector Functions, Regulation, and VaccinationPaula B. Kavathas, PhD, Peter J. Krause, MD, Nancy H. Ruddle, PhDChapter 6. Disorders of the Immune SystemPaula B. Kavathas, PhD, Peter J. Krause, MD, Nancy H. Ruddle, PhDChapter 7. Polymorphisms of Selected Components of the Innate and Adaptive Immune SystemsNancy H. Ruddle and Paula B. KavathasChapter 8. Immunoepidemiology of Immune DysfunctionEric Meffre, Peter J. Krause and Nancy H. RuddleChapter 9. Immunoepidemiology of Mycobacterium tuberculosisCamila D. Odio, Richard J. BucalaChapter 10. Host Immunity to Human immunodeficiency Virus InfectionElijah PaintsilChapter 11. Fungal ImmunoepidemiologyMarwan M. AzarChapter 12. Immunoepidemiology of Plasmodium falciparum MalariaAmy K. Bei, Sunil ParikhChapter 13. Immunoepidemiology of CancerXiaomei Ma, Rong WangChapter 14. Modeling Approaches Toward Understanding Infectious Disease TransmissionLaura A. Skrip, Jeffrey P. TownsendChapter 15. VaccinesDiane McMahon-PrattChapter 16. Immunotherapy for Infectious Diseases, Cancer, and AutoimmunityPeter J. Krause, Paula B. Kavathas, Nancy H. RuddleAppendixJane O'Bryan, Kara Fikrig, Peter J. Krause, Kate Nyhan

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Book SynopsisFocussing on stochastic models for the spread of infectious diseases in a human population, this book is the outcome of a two-week ICPAM/CIMPA school on "Stochastic models of epidemics" which took place in Ziguinchor, Senegal, December 5–16, 2015. The text is divided into four parts, each based on one of the courses given at the school: homogeneous models (Tom Britton and Etienne Pardoux), two-level mixing models (David Sirl and Frank Ball), epidemics on graphs (Viet Chi Tran), and statistics for epidemic models (Catherine Larédo). The CIMPA school was aimed at PhD students and Post Docs in the mathematical sciences. Parts (or all) of this book can be used as the basis for traditional or individual reading courses on the topic. For this reason, examples and exercises (some with solutions) are provided throughout.Table of Contents- Part I Stochastic Epidemics in a Homogeneous Community. - Introduction. - Stochastic Epidemic Models. - Markov Models. - General Closed Models. - Open Markov Models. - Part II Stochastic SIR Epidemics in Structured Populations. - Introduction. - Single Population Epidemics. - The Households Model. - A General Two-Level Mixing Model. - Part III Stochastic Epidemics in a Heterogeneous Community. - Introduction. - Random Graphs. - The Reproduction Number R0. - SIR Epidemics on Configuration Model Graphs. - Statistical Description of Epidemics Spreading on Networks: The Case of Cuban HIV. - Part IV Statistical Inference for Epidemic Processes in a Homogeneous Community. - Introduction. - Observations and Asymptotic Frameworks. - Inference for Markov Chain Epidemic Models. - Inference Based on the Diffusion Approximation of Epidemic Models. - Inference for Continuous Time SIR models.

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Book SynopsisThis book provides an extensive overview of the principles and methods of sample size calculation and recalculation in clinical trials. Appropriate calculation of the required sample size is crucial for the success of clinical trials. At the same time, a sample size that is too small or too large is problematic due to ethical, scientific, and economic reasons. Therefore, state-of-the art methods are required when planning clinical trials. Part I describes a general framework for deriving sample size calculation procedures. This enables an understanding of the common principles underlying the numerous methods presented in the following chapters. Part II addresses the fixed sample size design, where the required sample size is determined in the planning stage and is not changed afterwards. It covers sample size calculation methods for superiority, non-inferiority, and equivalence trials, as well as comparisons between two and more than two groups. A wide range of further topics is discussed, including sample size calculation for multiple comparisons, safety assessment, and multi-regional trials. There is often some uncertainty about the assumptions to be made when calculating the sample size upfront. Part III presents methods that allow to modify the initially specified sample size based on new information that becomes available during the ongoing trial. Blinded sample size recalculation procedures for internal pilot study designs are considered, as well as methods for sample size reassessment in adaptive designs that use unblinded data from interim analyses. The application is illustrated using numerous clinical trial examples, and software code implementing the methods is provided. The book offers theoretical background and practical advice for biostatisticians and clinicians from the pharmaceutical industry and academia who are involved in clinical trials. Covering basic as well as more advanced and recently developed methods, it is suitable for beginners, experienced applied statisticians, and practitioners. To gain maximum benefit, readers should be familiar with introductory statistics. The content of this book has been successfully used for courses on the topic.Trade Review“The R source code is shown by chapter, well-documented, and easy to find and follow as brief descriptions and necessary specifications for the function calls are given by means of comments. … a wide area of application fields is covered and exhaustive literature references for further reading are given. … The presentation of the material is very reader-friendly, easily accessible and pedagogical … . It is likewise highly recommended … . This is an effective and nicely written reference textbook.” (Oke Gerke, ISCB News, iscb.info, Vol. 72, December, 2021)Table of ContentsPart I Basics 1 Introduction 1.1 Background and outline 1.2 Examples 1.2.1 The ChroPac trial 1.2.2 The Parkinson trial 1.3 General considerations when calculating sample sizes 2 Statistical test and sample size calculation 2.1 The main principle of statistical testing 2.2 The main principle of sample size calculation Part II Sample size calculation 3 Comparison of two groups for normally distributed outcomes and test for difference or superiority 3.1 Background and notation 3.2 z-test 3.3 t-test 3.4 Analysis of covariance 3.5 Bayesian approach 3.5.1 Background 3.5.2 Methods 4 Comparison of two groups for continuous and ordered categorical outcomes and test for difference or superiority 4.1 Background and notation 4.2 Continuous outcomes 4.3 Ordered categorical outcomes 4.3.1 Assumption-free approach 4.3.2 Assuming proportional odds 5 Comparison of two groups for binary outcomes and test for difference and superiority 5.1 Background and notation 5.2 Asymptotic tests 5.2.1 Difference of rates as effect measure 5.2.2 Risk ratio as effect measure 5.2.3 Odds ratio as effect measure 5.2.4 Logistic regression 5.3 Exact unconditional tests 5.3.1 Background 5.3.2 Fisher-Boschloo test 6 Comparison of two groups for time-to-event outcomes and test for differences or superiority 6.1 Background and notation 6.1.1 Time-to-event data 6.1.2 Sample size calculation for time-to-event data 6.2 Exponentially distributed time-to-event data 6.3 Time-to-event data with proportional hazards 6.3.1 Approach of Schoenfeld 6.3.2 Approach of Freedman 7 Comparison of more than two groups and test for difference 7.1 Background and notation 7.2 Normally distributed outcomes 7.3 Continuous outcomes 7.4 Binary outcomes 7.4.1 Analysis with chi-square test 7.4.2 Analysis with Cochran-Armitage test 7.5 Time-to-event outcomes 8 Comparison of two groups and test for non-inferiority 8.1 Background and notation 8.2 Normally distributed outcomes 8.2.1 Difference of means 8.2.2 Ratio of means 8.3 Continuous and ordered categorical outcomes 8.4 Binary outcomes 8.4.1 Analysis with asymptotic tests 8.4.1.1 Difference of rates as effect measure 8.4.1.2 Risk ratio as effect measure 8.4.1.3 Odds ratio as effect measure 8.4.2 Exact unconditional tests 8.4.2.1 Background 8.4.2.2 Difference of rates as effect measure 8.4.2.3 Risk ratio as effect measure 8.4.2.4 Odds ratio as effect measure 8.5 Time-to-event outcomes 9 Comparison of three groups in the gold standard non-inferiority design 9.1 Background and notation 9.2 Net effect approach 9.3 Fraction effect approach 10 Comparison of two groups for normally distributed outcomes and test for equivalence 10.1 Background and notation 10.2 Difference of means 10.3 Ratio of means 11 Multiple comparisons 11.1 Background and notation 11.2 Generally applicable sample size calculation methods and applications 11.2.1 Methods 11.2.2 Applications 11.3 Multiple endpoints 11.3.1 Background and notation 11.3.2 Methods 11.4 More than two groups 11.4.1 Background and notation 11.4.2 Dunnett test 12 Assessment of safety 12.1 Background and notation 12.2 Testing hypotheses on the event probability 12.3 Estimating the occurrence probability of an event with specified precision 12.4 Observing at least one event 13 Cluster-randomized trials 13.1 Background and notation 13.2 Normally distributed outcomes 13.2.1 Cluster-level analysis 13.2.2 Individual-level analysis 13.2.3 Dealing with unequal cluster size 13.3 Other scale levels of the outcome 14 Multi-regional trials 14.1 Background and notation 14.2 Sample size calculation for demonstrating consistency of global results and results for a specified region 14.3 Sample size calculation for demonstrating a consistent trend across all regions 15 Integrated planning of phase II/III drug development programs 15.1 Background and notation 15.2 Optimizing phase II/III programs 16 Simulation-based sample size calculation Part III Sample size recalculation 17 Background Part IIIA Blinded sample size recalculation in internal pilot study designs 18 Background and notation 19 A general approach for controlling the type I error rate for blinded sample size recalculation 20 Comparison of two groups for normally distributed outcomes and test for difference or superiority 20.1 t-Test 20.1.1 Background and notation 20.1.2 Blinded variance estimation 20.1.3 Type I error rate 20.1.4 Power and sample size 20.2 Analysis of covariance 20.2.1 Background and notation 20.2.2 Blinded variance estimation 20.2.3 Type I error rate 20.2.4 Power and sample size 21 Comparison of two groups for binary outcomes and test for difference or superiority 21.1 Background and notation 21.2 Asymptotic tests 21.2.1 Difference of rates as effect measure 21.2.2 Risk ratio and odds ratio as effect measure 21.3 Fisher-Boschloo test 22 Comparison of two groups for normally distributed outcomes and test for non-inferiority 22.1 t-Test 22.1.1 Background and notation 22.1.2 Blinded variance estimation 22.1.3 Type I error rate 22.1.4 Power and sample size 22.2 Analysis of covariance 23 Comparison of two groups for binary outcomes and test for non-inferiority 23.1 Background and notation 23.2 Difference of rates as effect measure 23.3 Risk ratio and odds ratio as effect measure 24 Comparison of two groups for normally distributed outcomes and test for equivalence 25 Regulatory and operational aspects 26 Concluding remarks Part IIIB Unblinded sample size recalculation in adaptive designs 27 Background and notation 27.1 Group-sequential designs 27.2 Adaptive designs 27.2.1 Combination function approach 27.2.2 Conditional error function approach 28 Sample size recalculation based on conditional power 28.1 Background and notation 28.2 Using the interim estimate of the effect 28.3 Using the initially specified effect 28.4 Using prior information as well as the interim effect estimate 29 Sample size recalculation by optimization 30 Regulatory and operational aspects 31 Concluding remarks Appendix: Selected R software code References

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Book SynopsisCOVID-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting the virus, enormously tap into the power of artificial intelligence and its predictive models for urgent decision support. This book showcases a collection of important predictive models that used during the pandemic, and discusses and compares their efficacy and limitations. Readers from both healthcare industries and academia can gain unique insights on how predictive models were designed and applied on epidemic data. Taking COVID19 as a case study and showcasing the lessons learnt, this book will enable readers to be better prepared in the event of virus epidemics or pandemics in the future.Trade Review“This book is … of great interest for mathematical modelers--it nicely summarizes many important tools, with concrete examples, that could be adapted for other situations. … I strongly recommend this book to advanced undergraduate engineers and mathematicians as well as specialists dealing with dynamical system modeling.” (Arturo Ortiz-Tapia, Computing Reviews, July 26, 2022)Table of ContentsChapter 1. Prediction for Decision Support during the COVID-19 Pandemic.- Chapter 2. Epidemiology Compartmental Models - SIR, SEIR and SEIR with Intervention.- Chapter 3. Forecasting COVID-19 Time Series based on an Auto Regressive Model.- Chapter 4. Nonlinear Prediction for the COVID-19 Data based on Quadratic Kalman Filtering.- Chapter 5. Artificial Intelligence Prediction for the COVID-19 Data based on LSTM Neural Networks and H2O AutoML.- Chapter 6. Predicting the Geographic Spread of the COVID-19 Pandemic: a case study from Brazil.

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Book SynopsisThis textbook and guide focuses on methodologies for bias analysis in epidemiology and public health, not only providing updates to the first edition but also further developing methods and adding new advanced methods. As computational power available to analysts has improved and epidemiologic problems have become more advanced, missing data, Bayes, and empirical methods have become more commonly used. This new edition features updated examples throughout and adds coverage addressing: Measurement error pertaining to continuous and polytomous variables Methods surrounding person-time (rate) data Bias analysis using missing data, empirical (likelihood), and Bayes methods A unique feature of this revision is its section on best practices for implementing, presenting, and interpreting bias analyses. Pedagogically, the text guides students and professionals through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and measurement errors, and subsequent sections extend these methods to probabilistic bias analysis, missing data methods, likelihood-based approaches, Bayesian methods, and best practices.Table of ContentsPart I: Introduction1 Introduction and Objectives1 Introduction 1.2 Nonrandomized Epidemiologic Research 1.3 The Treatment of Uncertainty in Nonrandomized Research 1.4 Objective 1.5 Conclusion 2 A Guide to Implementing Quantitative Bias Analysis 2.1 Introduction 2.2 Reducing Error 2.3 Reducing Error by Design 2.4 Reducing Error in the Analysis 2.5 Quantifying Error 2.6 Evaluating the Potential Value of Quantitative Bias Analysis2.7 Planning for Bias Analysis 2.8 Creating a Data Collection Plan for Bias Analysis 2.9 Creating an Analytic Plan for a Bias Analysis 2.10 Bias Analysis Techniques 2.11 Introduction to Inference 2.12 Conclusion 3 Data Sources for Bias Analysis 3.1 Bias Parameters 3.2 Internal Data Sources 3.3 Selection Bias 3.4 Uncontrolled Confounder 3.5 Information Bias 3.6 Limitations of Internal Validation Studies 3.7 External Data Sources 3.8 Selection Bias 3.9 Uncontrolled Confounder 3.10 Information Bias 3.11 SummaryPart II: Preliminary Methods to Adjust for Systematic Errors 4 Selection Bias 4.1 Introduction 4.2 Definitions and Terms4.3 Motivation for Bias Analysis 4.4 Sources of Data 4.5 Simple Correction for Differential Initial Participation 4.6 Simple Correction for Differential Loss-to-Follow-up4.7 Sensitivity Analysis of the Bias Analysis 4.7 Signed Directed Acyclic Graphs to Estimate the Direction of Bias 5 Uncontrolled Confounders 5.1 Introduction 5.2 Definitions and Terms5.3 Motivation for Bias Analysis 5.4 Sources of Data5.5 Introduction to Simple Bias Analysis 5.6 Implementation of Simple Bias Analysis5.7 Sensitivity Analysis of the Bias Analysis 5.8 Uncontrolled Confounder in the Presence of Effect Modification 5.9 Polytomous Confounders 5.10 Bounding the Bias Limits of Uncontrolled Confounding5.10 Signed Directed Acyclic Graphs to Estimate the Direction of Bias5.11 Uncontrolled Confounding with Continuous Outcome, Exposure, or Confounder 6 Misclassification 6.1 Introduction 6.2 Definitions and Terms6.3 Motivation for Bias Analysis6.4 Sources of Data6.5 Calculating Classification Bias Parameters from Validation Data6.6 Exposure Misclassification for Dichotomous Exposures6.7 Exposure Misclassification for Polytomous Exposures6.8 Disease Misclassification 6.9 Covariate Misclassification 6.10 Dependent Misclassification6.11 Sensitivity Analysis of the Bias Analysis6.12 Adjusting Standard Errors for Corrections 7 Measurement Error for Continuous Variables7.1 Introduction7.2 Definition and Terms7.3 Motivation for Bias Analysis7.4 Exposure Measurement error7.5 Outcome Measurement error7.6 Covariate Measurement Error7.7 Correlated errors 8 Multiple Bias Modeling 8.1 Introduction 8.2 Order of Bias Analyses8.3 Multiple Bias Analysis, Simple MethodsPart III: Methods to Incorporate Systematic and Random Errors 9 Bias Analysis by Simulation for Summary Level Data9.1 Introduction 9.2 Probability Distributions 9.3 Correlated Distributions 9.4 Analytic Approach 9.5 Exposure Misclassification Implementation9.6 Exposure Measurement Error Implementation 9.7 Uncontrolled Confounding Implementation 9.8 Selection Bias Implementation 10 Bias Analysis by Simulation for Record Level Data10.1 Introduction 10.2 Analytic Approach 10.3 Exposure Misclassification Implementation10.4 Exposure Measurement Error Implementation 10.5 Uncontrolled Confounding Implementation 10.6 Selection Bias Implementation 11 Combining Systematic and Random Error11.1 Analytic approximation11.2 Resampling approximation11.3 Bootstrapping 12 Bias Analysis by Missing Data Methods12.1 Introduction 12.2 Analytic Approach 12.3 Exposure Misclassification Implementation12.4 Exposure Measurement Error Implementation 12.5 Uncontrolled Confounding Implementation 12.6 Selection Bias Implementation 12.7 Combining Systematic and Random Error 13 Bias Analysis by Empirical Methods13.1 Introduction 13.2 Analytic Approach 13.3 Exposure Misclassification Implementation 13.4 Exposure Measurement Error Implementation13.5 Uncontrolled Confounding Implementation 13.6 Selection Bias Implementation 13.7 Combining Systematic and Random Error 14 Bias Analysis by Bayesian Methods14.1 Introduction 14.2 Analytic Approach 14.3 Exposure Misclassification Implementation 14.4 Exposure Measurement Error Implementation 14.5 Uncontrolled Confounding Implementation 14.6 Selection Bias Implementation 14.7 Combining Systematic and Random Error 15 Multiple Bias Modeling15.1 Multiple Bias Analysis, Probabilistic Methods15.2 Multiple Bias Analysis, Missing Data Methods15.3 Multiple Bias Analysis, Empirical Methods15.4 Multiple Bias Analysis, Bayesian Methods Part IV: Good Practices16 Good Practices for Quantitative Bias Analysis16.1 Selection of bias sources16.2 Selection of analytic strategies16.3 Selection of values to assign to bias parameters17 Presentation and Inference 17.1 Presentation of simple and multidimensional bias analyses17.2 Presentation of advanced bias analyses 17.3 Inference 17.4 Caveats and Cautions 18 References 19 Index

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Book SynopsisIntroduction Berube NCSU.- UNIT 1 WHY HESITANCY?.- 1.1 Vaccinations in the Shadow of Eugenics Schwartzman, NCSU.- 1.2 Beyond Tuskegee: Trust and African Americans in America's Health System Olatunde, NCSU.- 1.3 A Critical Analysis of Black Immunity Myths and their Deadly Consequences - Powell, Morgan State U.- UNIT 2 THE ANTI-SCIENCE AND ANTI-VACCINE MOVEMENTS.- 2.1. The Anti-science movement in 21st century America & Europe - Lunsford, U. Florida.- 2.2. Anti-vaxxers: Wakefield and the Autism Scare - Rodriguez, Weber State University.- 2.3. Cross-Partisan Conspiracy Theories: How Plandemic Fueled Vaccine Misinformation - Justice, U. Mississippi & Bricker, U. Kansas.- 2.4. Negative Online Support Groups Berube, NCSU.- 2.4.1. On-Line Anti-Vaccine Movements -Trend. Luisi, U. Missouri.- 2.4.2. Athletes, Entertainers, and Political Figures: Online Influencers and the Amplification of Anti-Vaccine Voices on Social Media - Eng & Kim, U. Georgia.- UNIT 3 PRELIMINARY DIAGNOSIS.- 3.1. Minds, Made Up or Malleable? Vaccine Refusal at the Eclipse of Public Health CommunicationPsychological Reactance Theory - Lee, CUNY.- 3.2. Existential Approaches To Vaccine Hesitancy: Terror Management - Harvell-Bowman, James Madison U.- UNIT 4 SECONDARY RISKS: SIDE EFFECTS AND FATIGUE. - 4.1 Secondary Effects of Covid-19 and Secondary Risks of Covid-19 Responses Ellinport, Klasa, Galaitsi, Well, Jarman & Cummings, U.S. Army ERDC. Side-Effects Memon, Ohio U.- 4.2.1. Side-e?ect: Thrombocytopenia - Memon, Ohio U.- 4.2.2. Side-e?ect: Guillain-Barré syndrome. Memon, Ohio U. 4.2.3. Side-e?ect: Myocarditis Becktel - NCSU.- 4.3. Another Side effect: Vaccination Fatigue Berube, NCSU.- UNIT 5. SECOND LEVEL DRIVERS IN VACCINE HESITANCY .- 5.1 Trypanophobia [Fear of needles] and Vaccine Hesitancy - Berube, NCSU.- 5.2 Fear of getting COVID from the vaccine (Breakthrough cases) - Edwards, Tarleton State U.- 5.3 Long COVID Memon, Ohio U.- 5.4 Concerns about the Safety of mRNA Vaccines - Rawlings, U of North Carolina. -  UNIT 6. MISCOMMUNICATION CHALLENGES.- 6.1 Aggressive COVID vaccine conspiracies - Lunsford, U. Florida.- 6.2 Exploring Emergency Use Authorizations, Operation Warp Speed, the Case of Mifepristone, and the Dynamics of Agency Decision-Making Crosswell, U. Nevada, Reno.- 6.3. CDC and COVID Miscommunication, Uncertainty, and Cognitive Closure - Edwards, Tarleton State. - Unit 7. UNCERTAINTIES AND MISUNDERSTANDINGS.- 1 Herd Immunity: Communicating Collective Action to Suppress COVID-19 in Two Nordic countries - Mello, Northeastern U.- 7.2 Wait and see and the Free Riders. - Berube, NCSU.- 7.3 The Politicization of COVID-19 and Implications for Public Health. - Carpenter & Wimpy, Arkansas State.- UNIT 8. CHECKS ON MISINFORMATION.- 8.1 Information Overload and the COVID-19 vaccine - Hatfield, U Houston-Downtown.- 8.2 Using PEN-3 to Evaluate the Effectiveness of the CDC's COVID-19 Vaccine Communication Toolkit for Community-Based Organizations on Promoting Clinical Trials for African Americans - Powell, Moye & Edmond, Morgan State U. & Howard U.- 8.3. Recommended Approaches For COVID-19 Vaccination/Booster Messaging: An Expert Survey Crosswell & Duan, U. Nevada, Reno & Bombara, Eastdil Secured.- UNIT 9. MESSAGING.- 9.1 Distrust Medical Professionals and Scientists and their Role in Hesitancy - Rainear, West Chester U of PA.- 9.2 Challenges for Health Departments Communicating on COVID vaccines: A Content Analysis of American Health Department Websites Seo & Kim, Sam Houston State U.- 9.3. The impact of the Anti-science on general and COVID-19 vaccine refusal across the U.S., Europe, and the low- and middle-income countries of Africa and Latin America - Johnson & Parrish-Sprowl, IUI.- UNIT 10. SPECIAL POPULATIONS10.1.   Parental attitudes and vaccinating their children - Carrion, U. Nevada, Los Vegas.- 10.2.  The Unique Challenges Faced by Indigenous Communities during the Pandemic. Beard and Schoenbauer, U. Minnesota, Duluth, Smiles, U. Victoria, and Villard.- 10.3.  Rural and other minority populations Carpenter & Wimpy, Arkansas State.- UNIT 11. CITIZEN ENGAGEMENT.- The Building is Closed but the Ministry's Open: The Role of the Black Church Leaders in Vaccine Communication Through Black Podcast - Powell, Morgan State U. & Sumner, Howard U.- 11.2 Vaccines and Publics: A Variety of Engaging Experiences - Lee, CCNY.- 11.3 Public and Citizen Science - Rainear, West Chester U. of PA.- UNIT 12. RECOMMENDATIONS.- Rebuilding Government Trust: Harnessing Communication for Change. - Becktel, NCSU.- 12.2 Cultivating Epistemic and Argumentative Resilience to Reduce Vaccine Hesitancy Schwartzman, NCSU.- 12.3 Vaccination is a Public Strategy not a Personal One: Rebuilding Community - Berube, NCSU.- CONCLUSION Berube, NCSU.

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Book SynopsisDas Rahmenthema der 25. Jahrestagung der Deutschen Gesellschaft fur Medizinische DOkumentation, Informatik und Statistik e. V. - Nachsorge und Krankheitsverlaufsanalyse - hat einen engen Bezug zu aktuellen Problemen des Gesundheitswesens. Insbesondere in Kliniken, in denen schwere Erkrankungen mit modernen erfolgversprechenden Massnahmen be- handelt werden, wird die Notwendigkeit einer systematischen weiteren Uberwachung dieser Patienten immer dringlicher erachtet. Die Beob- achtung des weiteren Schicksals, die arztliche Betreuung und die Bewertung der zeitlichen Verlaufe ergeben Ansatzpunkte fur die weitere Verbesserung der Therapie. Diese Aufgabe lasst sich nur be- waltigen, wenn die dabei auftretenden Probleme der planvollen Doku- mentation, der Informationsubermittlung, der Datenspeicherung und der statistischen Auswertung von den Vertretern unseres Faches aktiv in Angriff genommen werden. Nach 25 Jahren einer sturmischen techno- logischen und Methodenentwicklung ist unsere junge medizinische Dis- ziplin in der Lage - wenn die erforderliche apparative und perso- nelle Ausstattung zur Verfugung steht -, die Probleme der Nachsorge und Krankheitsverlaufsanalyse in Zusammenarbeit mit Klinikern und Allgemeinmedizinern wirksam zu bearbeiten und Ergebnisse zu zeitigen, die fur den Arzt relevant sind. Die Tagung soll dazu Anregungen ver- mitteln und Losungswege aufzeigen. Funf Workshops und apparative, organisatorische und methodische Pro- bleme runden den Bezug unserer Arbeit auf die Probleme der !1edizin von heute ab. Erlangen hat eine traditionelle Verbundenheit mit der technologischen Entwicklung in der Medizin. Auch heute besteht, sowohl in der Medi- zinischen wie in der Technischen Fakultat, auf deren Campus wir tragen, ein waches Interesse fur den humanen Einsatz technologischer Neuerungen, insbesondere auch der elektronischen Datenverarbeitung. L.Table of ContentsErÖffnung der 25. Jahrestagung der Deutschen Gesellschaft FÜr Medizinische Dokumentation, Informatik und Statistik.- Zufall und lebendiges Geschehen.- Nachsorge und Krankheitsverlaufsanalyse-Einführung in die Thematik.- Nachsorge nach Krebsoperationen.- Probleme der Verlaufsbeobachtung und der prognostischen Beurteilung bei Herzkrankheiten.- Prognosestellung beim Rektumkarzinom mit Hilfe des COX-Modells.- Mathematische Modelle zur Analyse des Krankheitsverlaufs von Patienten mit Hirntumoren.- Analyse des Krankheitsverlaufs bei Prostatakarzinompatienten.- Statistische Auswertung des Krankheitsverlaufs von Tumorpatienten am Beispiel einer Studie über Karzinome der Mundhöhle.- Mehrkompartiment-Modelle in der Carcinogenese:Numerische Realisierung der Kleinste-Quadrate-Anpassung von Konzentrationsmessungen in der Maus.- Latenzzeitmessung bei Krebs am Beispiel einer Seite Fall-Kontroll-Studie an Lymphom- und Leukämiefällen in der amerikanischen Reifen- und Gummiindustrie.- Verlaufsuntersuchungen bei oralen Leukoplakien und Carcinomen.- Verteilungsfreie Teststatistiken bei Zen- sorierten Daten - Neue Entwicklungen.- Mathematisches Modell zur Prognose des Krankheitsverlaufs der Hepatitis B.- Methodische Probleme bei Langzeitstudien; insbesondere das Problem des Therapie-Abbruchs.- Nichtparametrischer Vergleich zweier Scharen von Verlaufskurven.- Anwendung eines Kompartimentmodelles zur Beurteilung von Behandlungsmethoden.- Probleme der statistischen Analyse einer Kohlenhydrat-Infusionsstudie.- Parametrische Tests für den Vergleich von Mittelwertsprofilen bei unverbundenen Beobachtungen mit homogenen Varianzen.- Variabilitätsuntersuchungen wesentlicher Spektralparameter im Verlaufe von EEG- Routine-Ableitungen.- Alternativen zur Bonferroni-Prozedur.- Variablenselektion bei multinomialen Klassifikationsproblemen.- Zur Problematik der Beurteilung abhängiger Häufigkeiten.- Explorative Datenanalyse - Schlußfolgerungen aus der Frühjahrstagung.- Die Integration der Nachsorgeorganisation und der Krankheitsverlaufsorganisation in ein allgemeines Befunddokumentationssystem.- Computerunterstützte Nachsorge und Krankheitsverlaufsanalyse – eine Komponente des medizinischen Auswertungssystems WAMAS.- Basisfunktionen für die Analyse von Verlaufsdaten.- ZEISIG Zytologisches Erfassungs- und Informationssystem in der Gynäkologie.- Betriebsärztliche Informationssysteme Schlußfolgerungen aus der Frühjahrstagung 1980.- Paket-Konzept und Refinement-Konstrukt Erste Erfahrungen mit einem Software-Entwicklungs-Instrument.- Verfahren zur Vereinheitlichung der Darstellung und Speicherung von Laborresultaten.- Implementierung eines Datenmodells auf einer operativen Intensivstation.- Das computergestützte Nachsorgesystem der I. Chirutgischen Universitätsklinik in Wien.- Computer-gestützte Nachsorge von Schrittmacher-Patienten.- Befunddokumentation in der hämostaseologischen Ambulanz der Medizinischen Hochschule Hannover.- Zur Frage des Aussagewertes einer routinemäßigen Thoraxübersichtsaufnahme bei der Diagnostik des Emphysems der Quarzstaublunge und dem Cor Pulmonale.- Auswertung von Krankheitsverläufen - Probleme und Lösungsmöglichkeiten: Dargestellt am Beispiel der akuten Virushepatitis.- Stoffwechselmetaboliten-Verlauf unter 48-stündiger Dauerinfusion von Glukose allein und in Mischung mit Sorbit, Fruktose oder Xylit bei Diabetikern.- EDV- Einsatz für die Bakteriologische Verlaufsund Befunddokumentation.- Erfassen und Auswerten von Antibiogrammen.- Institutionskarrieren schizophrener Kranker.- Das Fallregister psychisch Behinderter am PLK Weinsberg. Konzeption, Realisierung und erste Erfahrungen.- Prognose und Probleme der Verlaufsbeobachtung fokaler zerebraler Ischämie/Infarkte bei jungen Erwachsenen.- Langzeitverlauf nach Karotis-Operationen: Bedeutung der Neuropsychiatrischen Symptomatik.- Neuere Entwicklungen und Technologische Möglichkeiten der Mikroelektronik.- Ein Mikrorechner für die Eingliederung eines Analysenautomaten in dezentral organisierte Laborautomatisierungssysteme.- Mikroprozessoreinsatz im Physiologischen Labor.- Zur Bestimmung der Pulswellengeschwindigkeit.- On-line Verarbeitung von Hämoglobin-Reflexionsspektren hoher Repetitionsraten.- Anforderung an ein Mikroprozessorsystem zur Biosignalverarbeitung.- Entwurf und Aufbau eines Mikroprozessorsystems zur Biosignalverarbeitung.- Ein Mikrocomputer als Subsystem im 24-Stunden Betrieb.- Der Mikroprozessor als integrierender Bestandteil eines autonomen Meßplatzes im klinischen Laboratorium.- Implementierung des Programmes HES EKG in vor Ort auswertende Mikroprozessoren.- Erfahrungen im 3-jährigen Einsatz eines dezentralen Dokumentations- und Auskunftssystems für chronisch Kranke mit einem Minicomputer.- Ergebnisbericht der Moderatoren: Workshop 1 Mikroelektronik in der Medizin.- Die Basisdokumentation für Tumorkranke der Arbeitsgemeinschaft Deutscher Tumorzentren (ADT).- Das klinische Krebsregister des Tumorzentrums Köln.- Das Register für Onkologische Nachsorge der GBK in Münster.- Bericht über ein computergestütztes klinischpathologisches Krebsregister der ersten Ausbaustufe.- Ein klinisches Krebsregister als Basis für Nachsorge und statistische Auswertung – ein Erfahrungsbericht.- Das Dokumentations-, Kommunikations- und Organisations-System des Tumorzentrums Heidelberg/ Mannheim mit KRAZTUR.- Computerunterstützte Nachsorge und Basisdokumentation in der Radioonkologie.- Ein Patienteninformationssystem für die Strahlentherapie – Nachsorgeorganisation und Langzeitanalyse -.- Kooperative Dokumentation von Malignomen im Kindesalter.- Computerunterstütztes Magenbiopsieregister.- Computergestützte Erfassung und Nachsorge von Patienten mit kolorektalen Polypen.- Ergebnisbericht der Moderatoren: Workshop 2 Dokumentation, Datenverarbeitung und Statistik in medizinischen Krebszentren.- Therapiestudien im Kontext der Evaluationsforschung.- Organisatorische und methodische Probleme bei der Durchführung kontrollierter Psychopharmakastudien in der Praxis niedergelassener Ärzte.- Methodology and results of a long-term, controlled study of the effectiveness of immunosuppressive treatment of multiple sclerosis.- Der Wirksamkeitsnachweis in der Therapie des Ovarialkarzinoms.- Strategien zum Abbruch von kontrollierten Therapiestudien - Probleme und gegenwärtig diskutierte Ansätze.- Integrierung von Beobachtungen aus dem nichtärztlichen Bereich in die Krankheitsverlaufsanalysen.- Ergebnisbericht der Moderatoren: Workshop 3 Kontrollierte klinische Studien.- Klinische Datenverarbeitung in der Fakultät für Medizin der Technischen Universität München.- Klinische Basisdokumentation als Teil eines Informations-Systems in einem Rehabilitations-Krankenhaus Konzeption und Implementierung.- Klinische Dokumentation an einer Neurochirurgischen Klinik.- Dialogunterstützte klinische Dokumentation am Universitätsklinikum Göttingen.- Ergebnisbericht der Moderatoren: Workshop 4 Dokumentation und Verarbeitung klinischer Daten.- Verwaltung und Krankenhaus-Informationssystem Eine Strukturanalyse.- Untersuchung zur Inanspruchnahme eines Universitäts klinikums im stationären und ambulanten Bereich - durchgeführt an den Universitätskliniken Marburg.- Sind “Kurzlieger” einer Medizinischen Klinik für die Unterbringung in Hostelbetten geeignet? Die Bedeutung der Diagnosestatistik bei einer Planungsaufgabe.- Personalbedarfsplanung für den Krankenhaus-Pflegebereich mit Modellen der linearen Programmierung.- Lagerhaltung verderblicher medizinischer Güter.- Bedarfsgesteuerte Blutspenden mit TRAMIDIS.- Ergebnisbericht der Moderatoren: Workshop 5 Medizinökonomie.- Autorenverzeichnis.

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Book SynopsisVielfach genutzt für die Verarbeitung von Daten in Tabellenform, war Excel bisher für statistische Analysen weniger geeignet. Seit 2009 kann mit dem Add-In RExcel die Statistiksoftware R eingebunden werden. Der Band bietet die erste Einführung auf Deutsch zur Benutzung der RExcel-Oberfläche. Anhand eines Beispieldatensatzes aus der Herz-Kreislaufforschung werden Deskriptive Statistik, Korrelation und Regression, statistische Tests, Überlebenszeitanalyse sowie Fallzahlplanung nachvollziehbar dargestellt. Mit Schritt-für Schritt-Anleitungen und Tipps.Table of ContentsAllgemeine Information.- RExcel starten und beenden.- Datenverwaltung mit Excel und RExcel.- Datenmanagement in Excel bzw. R-Commander.- Vorbemerkungen und Informationen zur statistischen Analyse medizinischer Daten.- Deskriptive Statistik.- Zusammenhang von Variablen.- Statistische Tests für unabhängige Beobachtungen.- Statistische Tests für abhängige Beobachtungen.- Einstichprobentests und Konfidenzintervalle.- Überlebenszeitanalyse.- Fallzahlberechnungen.- Anhang.- Stichwortverzeichnis.

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Book SynopsisThis book offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a comprehensive description of the general data analysis paradigm, from exploratory analysis (principal component analysis, self-organizing maps and clustering) to modeling (classification, regression) and validation (including variable selection). It also includes a special section discussing several more specific topics in the area of chemometrics, such as outlier detection, and biomarker identification. The corresponding R code is provided for all the examples in the book; and scripts, functions and data are available in a separate R package. This second revised edition features not only updates on many of the topics covered, but also several sections of new material (e.g., on handling missing values in PCA, multivariate process monitoring and batch correction). Table of ContentsIntroduction. - Data.- Preprocessing.- Principal Component Analysis.- Self-Organizing Maps. - Clustering.- Classification.- Multivariate Regression. - Validation.- Variable Selection.- Chemometric Applications.

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Book SynopsisIn diesem Open Access Tagungsband beantworten renommierte Wissenschaftlerinnen und Wissenschaftler hochaktuelle Fragen zu Pandemien im Allgemeinen und der COVID-19-Pandemie im Besonderen. Vor allem die Auswirkungen auf unsere Gesellschaft stehen im interdisziplinären Fokus. Wie wirken Infektionserreger auf Körper und Psyche? Wie wirken Pandemien auf Städte und Demokratien? Beeinflussen mediale Diskurse und Seuchennarrative die Impfbereitschaft? Kann es eine Güterabwägung zwischen Gesundheitsschutz und Wirtschaftswachstum geben? Verletzen Lockdown und Maskenpflicht die Grundrechte? Auf dem Symposium Infektionen und Gesellschaft der Akademie der Wissenschaften in Hamburg postulierte Professor Ansgar W. Lohse: „Das Coronavirus hat uns nicht gänzlich überrascht: Die Gefahr katastrophaler Pandemien hat immer schon existiert und wächst weiter. Umso wichtiger ist es, dass wir aus der Vergangenheit für die Zukunft lernen.“ Der vorliegende Band bietet nicht nur eine interdisziplinäre Auseinandersetzung mit SARS-CoV-2 und den gesellschaftlichen Folgen, sondern auch einen Ausblick, wie wir in Zukunft mit Pandemien umgehen können – von der Prävention über die Entwicklung von Impfstoffen und Impfstrategien bis zu Therapiemöglichkeiten. Table of Contents1. Grußwort.-2. Eröffnung.-3. Risikoethische und entscheidungstheoretische Aspekte des Infektionsschutzes.-4. Impfstoffe gegen neu auftretende Viren.-5. Die Schwierigkeiten bei der Modellierung von Epidemien.-6. Angst vor Infektionen – Angst vor dem Impfen.-7. Spiegelung von Seuchen in Öffentlichkeit und Kultur.-8. Was lernen wir aus historischen Seuchen?.-9. Corona und die veränderte Wahrnehmung der Stadt.-10. Psychische Folgen von Pandemiemaßnahmen.-11. Tierseuchen und One Health.-12. Ökonomische Folgen von Pandemien.-13. Verfassungsrechtliche Grenzen des Infektionsschutzes.-14. Ethische Abwägungen beim Infektionsschutz.-15. Die Rolle der Medien in der COVID-19-Pandemie.-16. Wie entwickeln wir Impfstoffe, Impfstrategien und Therapien gegen Infektionskrankheiten für die Zukunft?.

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