Bayesian Design in Clinical Trials

• Autore: Berchialla Paola
• Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 online resource (190 p.)
• Soggetto topico: Humanities; Social interaction
• Soggetto non controllato: adaptive designs; adaptive randomization; Bayesian; Bayesian designs; bayesian inference; Bayesian inference; bayesian meta-analysis; Bayesian monitoring; Bayesian trial; Bayesian trial design; binary data; bridging studies; causal inference; cisplatin; clinical trial; clinical trials; clustering; combination study; distribution distance; dose escalation; dose-escalation; dose-finding; dose-response; doubly robust estimation; doxorubicin; early phase dose finding; frequentist validation; futility rules; highest posterior density intervals; interaction; interim analysis; latent dirichlet allocation; meta-analysis; modelling assumption; normal approximation; oncology; optimal dose combination; oxaliplatin; peritoneal carcinomatosis; phase I; PIPAC; poor accrual; posterior and predictive probabilities; power-prior; predictive analysis; predictive power; prior distribution; prior elicitation; priors; propensity score; randomized controlled trial; rare disease; sample size; sample size determination; stopping boundaries; target allocation; treatment combinations
• Persona (resp. second.): BaldiIleana; BerchiallaPaola
• Sommario/riassunto: 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.
• Titolo autorizzato: Bayesian Design in Clinical Trials
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557608103321
• Lo trovi qui: Univ. Federico II