Bayesian Design in Clinical Trials

In the last decade, the number of clinical trials using Bayesian methods has grown dramatically. Nowadays, regulatory authorities appear to be more receptive to Bayesian methods than ever. The Bayesian methodology is well suited to address the issues arising in the planning, analysis, and conduct of...

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Year of Publication:2022
Language:English
Physical Description:1 electronic resource (190 p.)
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spelling Berchialla, Paola edt
Bayesian Design in Clinical Trials
Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
1 electronic resource (190 p.)
text txt rdacontent
computer c rdamedia
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In the last decade, the number of clinical trials using Bayesian methods has grown dramatically. Nowadays, regulatory authorities appear to be more receptive to Bayesian methods than ever. The Bayesian methodology is well suited to address the issues arising in the planning, analysis, and conduct of clinical trials. Due to their flexibility, Bayesian design methods based on the accrued data of ongoing trials have been recommended by both the US Food and Drug Administration and the European Medicines Agency for dose-response trials in early clinical development. A distinctive feature of the Bayesian approach is its ability to deal with external information, such as historical data, findings from previous studies and expert opinions, through prior elicitation. In fact, it provides a framework for embedding and handling the variability of auxiliary information within the planning and analysis of the study. A growing body of literature examines the use of historical data to augment newly collected data, especially in clinical trials where patients are difficult to recruit, which is the case for rare diseases, for example. Many works explore how this can be done properly, since using historical data has been recognized as less controversial than eliciting prior information from experts’ opinions. In this book, applications of Bayesian design in the planning and analysis of clinical trials are introduced, along with methodological contributions to specific topics of Bayesian statistics. Finally, two reviews regarding the state-of-the-art of the Bayesian approach in clinical field trials are presented.
English
Humanities bicssc
Social interaction bicssc
dose-escalation
combination study
modelling assumption
interaction
adaptive designs
adaptive randomization
Bayesian designs
clinical trials
predictive power
target allocation
Bayesian inference
highest posterior density intervals
normal approximation
predictive analysis
sample size determination
bayesian meta-analysis
clustering
binary data
priors
frequentist validation
Bayesian
rare disease
prior distribution
meta-analysis
sample size
bridging studies
distribution distance
oncology
phase I
dose-finding
dose–response
bayesian inference
prior elicitation
latent dirichlet allocation
clinical trial
power-prior
poor accrual
Bayesian trial
cisplatin
doxorubicin
oxaliplatin
dose escalation
PIPAC
peritoneal carcinomatosis
randomized controlled trial
causal inference
doubly robust estimation
propensity score
Bayesian monitoring
futility rules
interim analysis
posterior and predictive probabilities
stopping boundaries
Bayesian trial design
early phase dose finding
treatment combinations
optimal dose combination
3-0365-3333-8
Baldi, Ileana edt
Berchialla, Paola oth
Baldi, Ileana oth
language English
format eBook
author2 Baldi, Ileana
Berchialla, Paola
Baldi, Ileana
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Berchialla, Paola
Baldi, Ileana
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author2_role HerausgeberIn
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title Bayesian Design in Clinical Trials
spellingShingle Bayesian Design in Clinical Trials
title_full Bayesian Design in Clinical Trials
title_fullStr Bayesian Design in Clinical Trials
title_full_unstemmed Bayesian Design in Clinical Trials
title_auth Bayesian Design in Clinical Trials
title_new Bayesian Design in Clinical Trials
title_sort bayesian design in clinical trials
publisher MDPI - Multidisciplinary Digital Publishing Institute
publishDate 2022
physical 1 electronic resource (190 p.)
isbn 3-0365-3333-8
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