LEADER 04272nam 2200673Ia 450 001 9910807325103321 005 20240516182111.0 010 $a1-118-31457-3 010 $a1-280-77255-7 010 $a9786613683328 010 $a1-118-31456-5 010 $a1-119-94241-1 010 $a1-119-94240-3 035 $a(CKB)2670000000205652 035 $a(EBL)943817 035 $a(OCoLC)778991200 035 $a(SSID)ssj0000676779 035 $a(PQKBManifestationID)11437243 035 $a(PQKBTitleCode)TC0000676779 035 $a(PQKBWorkID)10683858 035 $a(PQKB)11701701 035 $a(MiAaPQ)EBC943817 035 $a(Au-PeEL)EBL943817 035 $a(CaPaEBR)ebr10570719 035 $a(CaONFJC)MIL368332 035 $a(PPN)263078922 035 $a(EXLCZ)992670000000205652 100 $a20120216d2012 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian biostatistics /$fEmmanuel Lesaffre, Andrew B. Lawson 205 $a1st ed. 210 $aChichester, West Sussex $cJohn Wiley & Sons$d2012 215 $a1 online resource (536 pages) 225 1 $aStatistics in practice 300 $aDescription based upon print version of record. 311 $a0-470-01823-2 320 $aIncludes bibliographical references and index. 327 $aBasic 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. 330 $aThe 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. 330 $aKey Features: Provides an authoritative account of Bayesian methodology, from its most basic elements to its practical implementation, with an emphasis on healthcare techniques.Contains introductory explanations of Bayesian principles common to all areas of application.Presents clear and concise examples in biostatistics applications such as clinical trials, longitudinal studies, bioassay, survival, image analysis and bioinformatics.Illustrated throughout with examples using software including WinBUGS, OpenBUGS, SAS and various dedicated R programs.Highlights the differences between the Bayesian and classical approaches.Supported by an accompanying website hosting free software and case study guides.Bayesian Biostatistics introduces the reader smoothly into the Bayesian statistical methods with chapters that gradually increase in level of complexity. Master students in biostatistics, applied statisticians and all researchers with a good background in classical statistics who have interest in Bayesian methods will find this book useful. 410 0$aStatistics in practice. 606 $aBiometry$xMethodology 606 $aBayesian statistical decision theory 615 0$aBiometry$xMethodology. 615 0$aBayesian statistical decision theory. 676 $a570.1/5195 700 $aLesaffre$b Emmanuel$01628639 701 $aLawson$b Andrew$g(Andrew B.)$0149118 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910807325103321 996 $aBayesian biostatistics$94030018 997 $aUNINA