01121nam0 22002891i 450 UON0016447320231205103024.69834-87108-46-120021113d1999 |0itac50 baitaIT|||| |||||Fragmenta omnia quae extant. Pars 1.: SupplementumM. Terenti Varroniscollegit recensuitque Marcello SalvadoreHildesheimOlms1999138 p.22 cm001UON001650382001 Bibliotheca weidmanniana4LETTERATURA LATINAUONC026272FIDEHildesheimUONL000317870Letteratura latina21VARROMarcus TerentiusUONV05736071700SALVADOREMarcelloUONV057421ITSOL20240220RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00164473SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI T 2 VARRO 5001 02 SI MC 26210 5 02 Fragmenta omnia quae extant1245397UNIOR04672nam 2201057z- 450 991055760810332120220321(CKB)5400000000045321(oapen)https://directory.doabooks.org/handle/20.500.12854/79674(oapen)doab79674(EXLCZ)99540000000004532120202203d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierBayesian Design in Clinical TrialsBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (190 p.)3-0365-3333-8 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.HumanitiesbicsscSocial interactionbicsscadaptive designsadaptive randomizationBayesianBayesian designsbayesian inferenceBayesian inferencebayesian meta-analysisBayesian monitoringBayesian trialBayesian trial designbinary databridging studiescausal inferencecisplatinclinical trialclinical trialsclusteringcombination studydistribution distancedose escalationdose-escalationdose-findingdose-responsedoubly robust estimationdoxorubicinearly phase dose findingfrequentist validationfutility ruleshighest posterior density intervalsinteractioninterim analysislatent dirichlet allocationmeta-analysismodelling assumptionnormal approximationoncologyoptimal dose combinationoxaliplatinperitoneal carcinomatosisphase IPIPACpoor accrualposterior and predictive probabilitiespower-priorpredictive analysispredictive powerprior distributionprior elicitationpriorspropensity scorerandomized controlled trialrare diseasesample sizesample size determinationstopping boundariestarget allocationtreatment combinationsHumanitiesSocial interactionBerchialla Paolaedt1325099Baldi IleanaedtBerchialla PaolaothBaldi IleanaothBOOK9910557608103321Bayesian Design in Clinical Trials3036577UNINA