Bayesian adaptive methods for clinical trials
Material type: TextSeries: Publication details: Boca Raton : CRC Press, c2011Description: xvii, 305 p. : illISBN: 9781439825488 Subject(s): Clinical trials | Bayesian statistical decision theory | Clinical Trials as Topic | Bayes TheoremDDC classification: 615.507 24 Summary: "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.Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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BK | Kannur University Central Library Stack | Stack | 615.507 24 BER/B (Browse shelf (Opens below)) | Available | 34565 |
Browsing Kannur University Central Library shelves, Shelving location: Stack, Collection: Stack Close shelf browser (Hides shelf browser)
615.329 PRO/P Probiotic foods in health and disease | 615.40151 PUN/H Handbook of mathematics for pharmacy students | 615.40151 PUN/H Handbook of mathematics for pharmacy students | 615.507 24 BER/B Bayesian adaptive methods for clinical trials | 615.507 24 CLI Clinical trial data analysis using R | 615.507 24 FAI/D Design and analysis of quality of life studies in clinical trials | 615.507 24 INT Introduction to statistical 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- 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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