03685oam 2200637I 450 991045880200332120200520144314.00-429-15242-61-282-90299-797866129029941-4398-2551-310.1201/EBK1439825488 (CKB)2670000000055657(EBL)601268(OCoLC)668229601(SSID)ssj0000412544(PQKBManifestationID)11281111(PQKBTitleCode)TC0000412544(PQKBWorkID)10369216(PQKB)11405906(MiAaPQ)EBC601268(Au-PeEL)EBL601268(CaPaEBR)ebr10430730(CaONFJC)MIL290299(OCoLC)757918270(EXLCZ)99267000000005565720180331d2011 uy 0engur|n|---|||||txtccrBayesian adaptive methods for clinical trials /Scott M. Berry. [et al.]Boca Raton :Chapman & Hall/CRC,2011.1 online resource (316 p.)Chapman & Hall/CRC biostatistics series ;38Description based upon print version of record.1-4398-2548-3 Includes bibliographical references and indexes.Front 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 coverAs 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.Chapman & Hall/CRC biostatistics series ;38.Clinical trialsStatistical methodsBayesian statistical decision theoryElectronic books.Clinical trialsStatistical methods.Bayesian statistical decision theory.615.5072/4Berry Scott M934547MiAaPQMiAaPQMiAaPQBOOK9910458802003321Bayesian adaptive methods for clinical trials2104455UNINA