Bayesian adaptive methods for clinical trials - Info and Reading Options
By Scott M. Berry

"Bayesian adaptive methods for clinical trials" was published by Chapman & Hall/CRC in 2011 - Boca Raton and the language of the book is English.
“Bayesian adaptive methods for clinical trials” Metadata:
- Title: ➤ Bayesian adaptive methods for clinical trials
- Author: Scott M. Berry
- Language: English
- Publisher: Chapman & Hall/CRC
- Publish Date: 2011
- Publish Location: Boca Raton
“Bayesian adaptive methods for clinical trials” Subjects and Themes:
- Subjects: ➤ Statistical methods - Bayesian statistical decision theory - Clinical Trials as Topic - Clinical trials - Bayes Theorem - Études cliniques - Méthodes statistiques - Théorie de la décision bayésienne - Théorème de Bayes - HEALTH & FITNESS - Alternative Therapies - MEDICAL - Healing - BODY, MIND & SPIRIT - General
Edition Specifications:
- Pagination: p. ;
Edition Identifiers:
- The Open Library ID: OL24523924M - OL15571413W
- Online Computer Library Center (OCLC) ID: 668229601 - 639940808
- Library of Congress Control Number (LCCN): 2010022618
- ISBN-13: 9781439825488
- All ISBNs: 9781439825488
AI-generated Review of “Bayesian adaptive methods for clinical trials”:
"Bayesian adaptive methods for clinical trials" Table Of Contents:
- 1- (Publisher-supplied data) Statistical approaches for clinical trials
- 2- Basics of Bayesian inference
- 3- Phase I studies
- 4- Phase II studies
- 5- Phase III studies
- 6- Special topics.
"Bayesian adaptive methods for clinical trials" Description:
The Open Library:
"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.
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