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Bayesian adaptive methods for clinical trials /
"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 ełcient Markov chain Monte Carlo (MCMC) algo- rithms for sampling from and summarizing posterior distributions. Prac- titi...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press,
©2011.
|
| Colección: | Chapman & Hall/CRC biostatistics series ;
38. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Bayesian adaptive methods for clinical trials / |c Scott M. Berry [and others]. |
| 260 | |a Boca Raton : |b CRC Press, |c ©2011. | ||
| 300 | |a 1 online resource (xvii, 305 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 490 | 1 | |a Chapman & Hall/CRC biostatistics series ; |v 38 | |
| 504 | |a Includes bibliographical references and indexes. | ||
| 505 | 0 | |a 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 ełcient 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 ʻexibility and adaptivity of the Bayesian approach, which permits stopping for ełcacy, 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. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Clinical trials |x Statistical methods. | |
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 1 | 2 | |a Clinical Trials as Topic |
| 650 | 2 | 2 | |a Bayes Theorem |
| 650 | 6 | |a Études cliniques |x Méthodes statistiques. | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 6 | |a Théorème de Bayes. | |
| 650 | 7 | |a HEALTH & FITNESS |x Alternative Therapies. |2 bisacsh | |
| 650 | 7 | |a MEDICAL |x Healing. |2 bisacsh | |
| 650 | 7 | |a HEALTH & FITNESS |x Healing. |2 bisacsh | |
| 650 | 7 | |a BODY, MIND & SPIRIT |x Healing |x General. |2 bisacsh | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 650 | 7 | |a Clinical trials |x Statistical methods |2 fast | |
| 700 | 1 | |a Berry, Scott M. | |
| 758 | |i has work: |a Bayesian adaptive methods for clinical trials (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFR8VBbGP6hQqTYdjpjFYX |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |t Bayesian adaptive methods for clinical trials. |d Boca Raton : CRC Press, ©2011 |z 9781439825488 |w (DLC) 2010022618 |w (OCoLC)639940808 |
| 830 | 0 | |a Chapman & Hall/CRC biostatistics series ; |v 38. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=601268 |z Texto completo |
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