04272nam 2200673Ia 450 991080732510332120240516182111.01-118-31457-31-280-77255-797866136833281-118-31456-51-119-94241-11-119-94240-3(CKB)2670000000205652(EBL)943817(OCoLC)778991200(SSID)ssj0000676779(PQKBManifestationID)11437243(PQKBTitleCode)TC0000676779(PQKBWorkID)10683858(PQKB)11701701(MiAaPQ)EBC943817(Au-PeEL)EBL943817(CaPaEBR)ebr10570719(CaONFJC)MIL368332(PPN)263078922(EXLCZ)99267000000020565220120216d2012 uy 0engur|n|||||||||txtccrBayesian biostatistics /Emmanuel Lesaffre, Andrew B. Lawson1st ed.Chichester, West Sussex John Wiley & Sons20121 online resource (536 pages)Statistics in practiceDescription based upon print version of record.0-470-01823-2 Includes bibliographical references and index.Basic concepts in Bayesian methods -- Bayes theorem -- Posterior summary measures -- More than one parameter -- The prior distribution -- Markov chain Monte Carlo -- Software -- Hierarchical models -- Model building and assessment -- Variable selection -- Bioassay -- Measurement error -- Survival analysis -- Longitudinal analysis -- Disease mapping & image analysis -- Final chapter -- Distributions.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.Statistics in practice.BiometryMethodologyBayesian statistical decision theoryBiometryMethodology.Bayesian statistical decision theory.570.1/5195Lesaffre Emmanuel1628639Lawson Andrew(Andrew B.)149118MiAaPQMiAaPQMiAaPQBOOK9910807325103321Bayesian biostatistics4030018UNINA