Bayesian Biostatistics

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9780470018231: Bayesian Biostatistics
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The growth of biostatistics has been phenomenal in recent years andhas been marked by considerable technical innovation in bothmethodology and computational practicality. One area that hasexperienced significant growth is Bayesian methods. The growing useof Bayesian methodology has taken place partly due to an increasingnumber of practitioners valuing the Bayesian paradigm as matchingthat of scientific discovery. In addition, computational advanceshave allowed for more complex models to be fitted routinely torealistic data sets.

Through examples, exercises and a combination of introductoryand more advanced chapters, this book provides an invaluableunderstanding of the complex world of biomedical statisticsillustrated via a diverse range of applications taken fromepidemiology, exploratory clinical studies, health promotionstudies, image analysis and clinical trials.

Key Features:

  • Provides an authoritative account of Bayesian methodology, fromits most basic elements to its practical implementation, with anemphasis on healthcare techniques.
  • Contains introductory explanations of Bayesian principlescommon to all areas of application.
  • Presents clear and concise examples in biostatisticsapplications such as clinical trials, longitudinal studies,bioassay, survival, image analysis and bioinformatics.
  • Illustrated throughout with examples using software includingWinBUGS, OpenBUGS, SAS and various dedicated Rprograms.
  • Highlights the differences between the Bayesian and classicalapproaches.
  • Supported by an accompanying website hosting free softwareand case study guides.

Bayesian Biostatistics introduces the reader smoothlyinto the Bayesian statistical methods with chapters that graduallyincrease in level of complexity. Master students in biostatistics,applied statisticians and all researchers with a good background inclassical statistics who have interest in Bayesian methods willfind this book useful.

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About the Author:

Emmanuel Lesaffre, Professor of Statistics, BiostatisticalCentre, Catholic University of Leuven, Leuven, Belgium. Dr Lesaffrehas worked on and studied various areas of biostatistics for 25years. He has taught a variety of courses to students from manydisciplines, from medicine and pharmacy, to statistics andengineering, teaching Bayesian statistics for the last 5 years.Having published over 200 papers in major statistical and medicaljournals, he has also Co-Edited the book Disease Mapping andRisk Assessment for Public Health, and was the Associate Editorfor Biometrics. He is currently Co-Editor of the journal“Statistical Modelling: An International Journal”,Special Editor of two volumes on Statistics in Dentistry inStatistical Methods in Medical Research, and a member of theEditorial Boards of numerous journals.

Andrew Lawson, Professor of Statistics, Dept ofEpidemiology & Biostatistics, University of South Carolina,USA. Dr Lawson has considerable and wide ranging experience in thedevelopment of statistical methods for spatial and environmentalepidemiology. He has solid experience in teaching Bayesianstatistics to students studying biostatistics and has also writtentwo books and numerous journal articles in the biostatistics area.Dr Lawson has also guest edited two special issues of“Statistics in Medicine” focusing on Disease Mapping.He is a member of the editorial boards of the journals:Statistics in Medicine and .

Review:

“In conclusion, we consider the book by Lesaffre andLawson a noteworthy contribution to the dissemination of Bayesianmethods, and a good manual of reference for many common and somespecialized applications in biomedical research. The great varietyof examples and topics covered offers both advantages anddisadvantages. Some parts might be too specialized for statisticsstudents, but lecturers and applied statisticians will benefit alot from the authors’ wealth of experience.” (Biometrical Journal, 15 July 2013)

The book Bayesian Biostatisticsby Lesaffre andLawson, is a welcoming addition to this important area of researchin biostatistical applications. For example, in the area ofclinical trials, Bayesian methods provide flexibility and benefitsfor incorporating historical data with current data and then usingthe resulting posterior to make probability statements fordifferent outcomes”.(Journal ofBiopharmaceutical Statistics, 1 January 2013)

 

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Book Description John Wiley and Sons Ltd, United States, 2012. Hardback. Condition: New. 1. Auflage. Language: English. Brand new Book. 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. Seller Inventory # AAH9780470018231

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Book Description John Wiley and Sons Ltd, United States, 2012. Hardback. Condition: New. 1. Auflage. Language: English. Brand new Book. 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. Seller Inventory # AAH9780470018231

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