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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.

Humanities --- Social interaction --- 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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

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