Bayesian Thinking In Biostatistics
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Bayesian Thinking In Biostatistics
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Author : Gary L Rosner
language : en
Publisher: CRC Press
Release Date : 2021-03-15
Bayesian Thinking In Biostatistics written by Gary L Rosner and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-15 with Medical categories.
Praise for Bayesian Thinking in Biostatistics: "This thoroughly modern Bayesian book ...is a 'must have' as a textbook or a reference volume. Rosner, Laud and Johnson make the case for Bayesian approaches by melding clear exposition on methodology with serious attention to a broad array of illuminating applications. These are activated by excellent coverage of computing methods and provision of code. Their content on model assessment, robustness, data-analytic approaches and predictive assessments...are essential to valid practice. The numerous exercises and professional advice make the book ideal as a text for an intermediate-level course..." -Thomas Louis, Johns Hopkins University "The book introduces all the important topics that one would usually cover in a beginning graduate level class on Bayesian biostatistics. The careful introduction of the Bayesian viewpoint and the mechanics of implementing Bayesian inference in the early chapters makes it a complete self- contained introduction to Bayesian inference for biomedical problems....Another great feature for using this book as a textbook is the inclusion of extensive problem sets, going well beyond construed and simple problems. Many exercises consider real data and studies, providing very useful examples in addition to serving as problems." - Peter Mueller, University of Texas With a focus on incorporating sensible prior distributions and discussions on many recent developments in Bayesian methodologies, Bayesian Thinking in Biostatistics considers statistical issues in biomedical research. The book emphasizes greater collaboration between biostatisticians and biomedical researchers. The text includes an overview of Bayesian statistics, a discussion of many of the methods biostatisticians frequently use, such as rates and proportions, regression models, clinical trial design, and methods for evaluating diagnostic tests. Key Features Applies a Bayesian perspective to applications in biomedical science Highlights advances in clinical trial design Goes beyond standard statistical models in the book by introducing Bayesian nonparametric methods and illustrating their uses in data analysis Emphasizes estimation of biomedically relevant quantities and assessment of the uncertainty in this estimation Provides programs in the BUGS language, with variants for JAGS and Stan, that one can use or adapt for one's own research The intended audience includes graduate students in biostatistics, epidemiology, and biomedical researchers, in general Authors Gary L. Rosner is the Eli Kennerly Marshall, Jr., Professor of Oncology at the Johns Hopkins School of Medicine and Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Purushottam (Prakash) W. Laud is Professor in the Division of Biostatistics, and Director of the Biostatistics Shared Resource for the Cancer Center, at the Medical College of Wisconsin. Wesley O. Johnson is professor Emeritus in the Department of Statistics as the University of California, Irvine.
Bayesian Thinking In Biostatistics
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Author : Gary L Rosner
language : en
Publisher: CRC Press
Release Date : 2021-03-16
Bayesian Thinking In Biostatistics written by Gary L Rosner and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-16 with Mathematics categories.
Praise for Bayesian Thinking in Biostatistics: "This thoroughly modern Bayesian book ...is a 'must have' as a textbook or a reference volume. Rosner, Laud and Johnson make the case for Bayesian approaches by melding clear exposition on methodology with serious attention to a broad array of illuminating applications. These are activated by excellent coverage of computing methods and provision of code. Their content on model assessment, robustness, data-analytic approaches and predictive assessments...are essential to valid practice. The numerous exercises and professional advice make the book ideal as a text for an intermediate-level course..." -Thomas Louis, Johns Hopkins University "The book introduces all the important topics that one would usually cover in a beginning graduate level class on Bayesian biostatistics. The careful introduction of the Bayesian viewpoint and the mechanics of implementing Bayesian inference in the early chapters makes it a complete self- contained introduction to Bayesian inference for biomedical problems....Another great feature for using this book as a textbook is the inclusion of extensive problem sets, going well beyond construed and simple problems. Many exercises consider real data and studies, providing very useful examples in addition to serving as problems." - Peter Mueller, University of Texas With a focus on incorporating sensible prior distributions and discussions on many recent developments in Bayesian methodologies, Bayesian Thinking in Biostatistics considers statistical issues in biomedical research. The book emphasizes greater collaboration between biostatisticians and biomedical researchers. The text includes an overview of Bayesian statistics, a discussion of many of the methods biostatisticians frequently use, such as rates and proportions, regression models, clinical trial design, and methods for evaluating diagnostic tests. Key Features Applies a Bayesian perspective to applications in biomedical science Highlights advances in clinical trial design Goes beyond standard statistical models in the book by introducing Bayesian nonparametric methods and illustrating their uses in data analysis Emphasizes estimation of biomedically relevant quantities and assessment of the uncertainty in this estimation Provides programs in the BUGS language, with variants for JAGS and Stan, that one can use or adapt for one's own research The intended audience includes graduate students in biostatistics, epidemiology, and biomedical researchers, in general Authors Gary L. Rosner is the Eli Kennerly Marshall, Jr., Professor of Oncology at the Johns Hopkins School of Medicine and Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Purushottam (Prakash) W. Laud is Professor in the Division of Biostatistics, and Director of the Biostatistics Shared Resource for the Cancer Center, at the Medical College of Wisconsin. Wesley O. Johnson is professor Emeritus in the Department of Statistics as the University of California, Irvine.
Elementary Bayesian Biostatistics
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Author : Lemuel A. Moye
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Elementary Bayesian Biostatistics written by Lemuel A. Moye and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Mathematics categories.
Bayesian analyses have made important inroads in modern clinical research due, in part, to the incorporation of the traditional tools of noninformative priors as well as the modern innovations of adaptive randomization and predictive power. Presenting an introductory perspective to modern Bayesian procedures, Elementary Bayesian Biostatistics explo
Bayesian Biostatistics
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Author : Emmanuel Lesaffre
language : en
Publisher: John Wiley & Sons
Release Date : 2012-08-13
Bayesian Biostatistics written by Emmanuel Lesaffre and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-13 with Medical categories.
The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introductory and more advanced chapters, this book provides an invaluable understanding of the complex world of biomedical statistics illustrated via a diverse range of applications taken from epidemiology, exploratory clinical studies, health promotion studies, image analysis and clinical trials. Key Features: Provides an authoritative account of Bayesian methodology, from its most basic elements to its practical implementation, with an emphasis on healthcare techniques. Contains introductory explanations of Bayesian principles common to all areas of application. Presents clear and concise examples in biostatistics applications such as clinical trials, longitudinal studies, bioassay, survival, image analysis and bioinformatics. Illustrated throughout with examples using software including WinBUGS, OpenBUGS, SAS and various dedicated R programs. Highlights the differences between the Bayesian and classical approaches. Supported by an accompanying website hosting free software and case study guides. Bayesian Biostatistics introduces the reader smoothly into the Bayesian statistical methods with chapters that gradually increase in level of complexity. Master students in biostatistics, applied statisticians and all researchers with a good background in classical statistics who have interest in Bayesian methods will find this book useful.
Bayesian Methods In Epidemiology
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Author : Lyle D. Broemeling
language : en
Publisher: CRC Press
Release Date : 2013-08-13
Bayesian Methods In Epidemiology written by Lyle D. Broemeling and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-13 with Mathematics categories.
Written by a biostatistics expert with over 20 years of experience in the field, Bayesian Methods in Epidemiology presents statistical methods used in epidemiology from a Bayesian viewpoint. It employs the software package WinBUGS to carry out the analyses and offers the code in the text and for download online. The book examines study designs that investigate the association between exposure to risk factors and the occurrence of disease. It covers introductory adjustment techniques to compare mortality between states and regression methods to study the association between various risk factors and disease, including logistic regression, simple and multiple linear regression, categorical/ordinal regression, and nonlinear models. The text also introduces a Bayesian approach for the estimation of survival by life tables and illustrates other approaches to estimate survival, including a parametric model based on the Weibull distribution and the Cox proportional hazards (nonparametric) model. Using Bayesian methods to estimate the lead time of the modality, the author explains how to screen for a disease among individuals that do not exhibit any symptoms of the disease. With many examples and end-of-chapter exercises, this book is the first to introduce epidemiology from a Bayesian perspective. It shows epidemiologists how these Bayesian models and techniques are useful in studying the association between disease and exposure to risk factors.
Bayesian Data Analysis Third Edition
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Author : Andrew Gelman
language : en
Publisher: CRC Press
Release Date : 2013-11-01
Bayesian Data Analysis Third Edition written by Andrew Gelman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-01 with Mathematics categories.
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
A First Course In Bayesian Statistical Methods
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Author : Peter D. Hoff
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-06-02
A First Course In Bayesian Statistical Methods written by Peter D. Hoff and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-02 with Mathematics categories.
A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
Regression Modeling Strategies
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Author : Frank E. Harrell
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Regression Modeling Strategies written by Frank E. Harrell and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Mathematics categories.
Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".
Let The Evidence Speak
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Author : Alan Jessop
language : en
Publisher: Springer
Release Date : 2018-05-31
Let The Evidence Speak written by Alan Jessop and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-31 with Mathematics categories.
This book presents the most important ideas behind Bayes’ Rule in a form suitable for the general reader. It is written without formulae because they are not necessary; the ability to add and multiply is all that is needed. As well as showing in full the application of Bayes’ Rule to some quantitatively simple, though not trivial, examples, the book also convincingly demonstrates that some familiarity with Bayes’ Rule is helpful in thinking about how best to structure one’s thinking.