| 000 | 02934cam a2200349Ia 4500 | ||
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| 001 | CRC0KE11133PDF | ||
| 003 | BD-DhSAU | ||
| 005 | 20151012144309.0 | ||
| 006 | m|||||o||d|||||||| | ||
| 007 | cr|||| | ||
| 008 | 100602s2011 flu sb 001 0 eng d | ||
| 020 | _a9781439825518 (ebook : PDF) | ||
| 040 |
_aBD-DhSAU _cBD-DhSAU |
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| 090 |
_aR853.C55 _bB385 2011 |
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| 092 |
_a615.50724 _bB357 |
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| 245 | 0 | 0 |
_aBayesian adaptive methods for clinical trials _h[electronic resource] / _cScott M. Berry ... [et al.]. |
| 260 |
_aBoca Raton : _bChapman & Hall/CRC, _c2011. |
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| 300 | _a323 p. | ||
| 490 | 1 |
_aChapman & Hall/CRC biostatistics series ; _v38 |
|
| 504 | _aIncludes bibliographical references and indexes. | ||
| 505 | 0 | _a(Publisher-supplied data) Statistical approaches for clinical trials -- Basics of Bayesian inference -- Phase I studies -- Phase II studies -- Phase III studies -- Special topics. | |
| 520 | _a"As 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 e�cient 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 oʼer the simplest approach to hierarchical (random eʼects) 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 �exibility and adaptivity of the Bayesian approach, which permits stopping for e�cacy, 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. | ||
| 530 | _aAlso available in print edition. | ||
| 538 | _aMode of access: World Wide Web. | ||
| 650 | 0 |
_aClinical trials _xStatistical methods. |
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| 650 | 0 | _aBayesian statistical decision theory. | |
| 655 | 7 |
_aElectronic books. _2lcsh |
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| 700 | 1 | _aBerry, Scott M. | |
| 776 | 1 | _z9781439825488 | |
| 830 | 0 |
_aChapman & Hall/CRC biostatistics series ; _v38. |
|
| 856 | 4 | 0 |
_uhttp://marc.crcnetbase.com/isbn/9781439825518 _qapplication/PDF |
| 999 |
_c12122 _d12121 |
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