LEADER 03651oam 2200625I 450 001 9910785358203321 005 20230105191813.0 010 $a0-429-15242-6 010 $a1-282-90299-7 010 $a9786612902994 010 $a1-4398-2551-3 024 7 $a10.1201/EBK1439825488 035 $a(CKB)2670000000055657 035 $a(EBL)601268 035 $a(OCoLC)668229601 035 $a(SSID)ssj0000412544 035 $a(PQKBManifestationID)11281111 035 $a(PQKBTitleCode)TC0000412544 035 $a(PQKBWorkID)10369216 035 $a(PQKB)11405906 035 $a(MiAaPQ)EBC601268 035 $a(Au-PeEL)EBL601268 035 $a(CaPaEBR)ebr10430730 035 $a(CaONFJC)MIL290299 035 $a(OCoLC)757918270 035 $a(EXLCZ)992670000000055657 100 $a20180331d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aBayesian adaptive methods for clinical trials /$fScott M. Berry. [et al.] 210 1$aBoca Raton :$cChapman & Hall/CRC,$d2011. 215 $a1 online resource (316 p.) 225 1 $aChapman & Hall/CRC biostatistics series ;$v38 300 $aDescription based upon print version of record. 311 $a1-4398-2548-3 320 $aIncludes bibliographical references and indexes. 327 $aFront cover; Contents; Foreword; Preface; CHAPTER 1: Statistical approaches for clinical trials; CHAPTER 2: Basics of Bayesian inference; CHAPTER 3: Phase I studies; CHAPTER 4: Phase II studies; CHAPTER 5: Phase III studies; CHAPTER 6: Special topics; References; Back cover 330 $aAs has been well-discussed, the explosion of interest in Bayesian methods over the last 10 to 20 years has been the result of the convergence of modern computing power and eficient Markov chain Monte Carlo (MCMC) algo- rithms for sampling from and summarizing posterior distributions. Prac- titioners trained in traditional, frequentist statistical methods appear to have been drawn to Bayesian approaches for three reasons. One is that Bayesian approaches implemented with the majority of their informative content coming from the current data, and not any external prior informa- tion, typically have good frequentist properties (e.g., low mean squared er- ror in repeated use). Second, these methods as now readily implemented in WinBUGS and other MCMC-driven software packages now over the simplest approach to hierarchical (random erects) modeling, as routinely needed in longitudinal, frailty, spatial, time series, and a wide variety of other settings featuring interdependent data. Third, practitioners are attracted by the greater flexibility and adaptivity of the Bayesian approach, which permits stopping for efacacy, toxicity, and futility, as well as facilitates a straightforward solution to a great many other specialized problems such as dose-nding, adaptive randomization, equivalence testing, and others we shall describe. This book presents the Bayesian adaptive approach to the design and analysis of clinical trials--Provided by publisher. 410 0$aChapman & Hall/CRC biostatistics series ;$v38. 606 $aClinical trials$xStatistical methods 606 $aBayesian statistical decision theory 615 0$aClinical trials$xStatistical methods. 615 0$aBayesian statistical decision theory. 676 $a615.5072/4 701 $aBerry$b Scott M$01516838 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910785358203321 996 $aBayesian adaptive methods for clinical trials$93753548 997 $aUNINA