TY  - GEN
AU  - Berchialla,Paola
AU  - Baldi,Ileana
AU  - Berchialla,Paola
AU  - Baldi,Ileana
TI  - Bayesian Design in Clinical Trials
SN  - books978-3-0365-3333-9
PY  - 2022///
CY  - Basel
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - Humanities
KW  - bicssc
KW  - Social interaction
KW  - dose-escalation
KW  - combination study
KW  - modelling assumption
KW  - interaction
KW  - adaptive designs
KW  - adaptive randomization
KW  - Bayesian designs
KW  - clinical trials
KW  - predictive power
KW  - target allocation
KW  - Bayesian inference
KW  - highest posterior density intervals
KW  - normal approximation
KW  - predictive analysis
KW  - sample size determination
KW  - bayesian meta-analysis
KW  - clustering
KW  - binary data
KW  - priors
KW  - frequentist validation
KW  - Bayesian
KW  - rare disease
KW  - prior distribution
KW  - meta-analysis
KW  - sample size
KW  - bridging studies
KW  - distribution distance
KW  - oncology
KW  - phase I
KW  - dose-finding
KW  - dose-response
KW  - bayesian inference
KW  - prior elicitation
KW  - latent dirichlet allocation
KW  - clinical trial
KW  - power-prior
KW  - poor accrual
KW  - Bayesian trial
KW  - cisplatin
KW  - doxorubicin
KW  - oxaliplatin
KW  - dose escalation
KW  - PIPAC
KW  - peritoneal carcinomatosis
KW  - randomized controlled trial
KW  - causal inference
KW  - doubly robust estimation
KW  - propensity score
KW  - Bayesian monitoring
KW  - futility rules
KW  - interim analysis
KW  - posterior and predictive probabilities
KW  - stopping boundaries
KW  - Bayesian trial design
KW  - early phase dose finding
KW  - treatment combinations
KW  - optimal dose combination
N1  - Open Access
N2  - 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
UR  - https://mdpi.com/books/pdfview/book/5059
UR  - https://directory.doabooks.org/handle/20.500.12854/79674
ER  -