LEADER 00968nam0 22002771i 450 001 UON00035575 005 20231205102121.275 010 $a48-318-7136-2 100 $a20020107d1994 |0itac50 ba 101 $ajpn 102 $aJP 105 $a|||| 1|||| 200 1 $aNihon no kami to oken$fNakamura Ikuo 210 $aKyoto$cHozokan$d1994 215 $a6, 261 p.$d22 cm 606 $aRELIGIONI$xGIAPPONE$3UONC003882$2FI 620 $aJP$dKy?to$3UONL000059 686 $aGIA VII$cGIAPPONE - RELIGIONE E FILOSOFIA$2A 700 0$aNAKAMU Ikuo$3UONV022905$0646509 712 $aHozokan$3UONV249337$4650 801 $aIT$bSOL$c20240220$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00035575 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI GIA VII 061 N $eSI SA 88065 7 061 N 996 $aNihon no kami to oken$91194405 997 $aUNIOR LEADER 04672nam 2201057z- 450 001 9910557608103321 005 20220321 035 $a(CKB)5400000000045321 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/79674 035 $a(oapen)doab79674 035 $a(EXLCZ)995400000000045321 100 $a20202203d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aBayesian Design in Clinical Trials 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (190 p.) 311 08$a3-0365-3333-8 330 $aIn 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. 606 $aHumanities$2bicssc 606 $aSocial interaction$2bicssc 610 $aadaptive designs 610 $aadaptive randomization 610 $aBayesian 610 $aBayesian designs 610 $abayesian inference 610 $aBayesian inference 610 $abayesian meta-analysis 610 $aBayesian monitoring 610 $aBayesian trial 610 $aBayesian trial design 610 $abinary data 610 $abridging studies 610 $acausal inference 610 $acisplatin 610 $aclinical trial 610 $aclinical trials 610 $aclustering 610 $acombination study 610 $adistribution distance 610 $adose escalation 610 $adose-escalation 610 $adose-finding 610 $adose-response 610 $adoubly robust estimation 610 $adoxorubicin 610 $aearly phase dose finding 610 $afrequentist validation 610 $afutility rules 610 $ahighest posterior density intervals 610 $ainteraction 610 $ainterim analysis 610 $alatent dirichlet allocation 610 $ameta-analysis 610 $amodelling assumption 610 $anormal approximation 610 $aoncology 610 $aoptimal dose combination 610 $aoxaliplatin 610 $aperitoneal carcinomatosis 610 $aphase I 610 $aPIPAC 610 $apoor accrual 610 $aposterior and predictive probabilities 610 $apower-prior 610 $apredictive analysis 610 $apredictive power 610 $aprior distribution 610 $aprior elicitation 610 $apriors 610 $apropensity score 610 $arandomized controlled trial 610 $arare disease 610 $asample size 610 $asample size determination 610 $astopping boundaries 610 $atarget allocation 610 $atreatment combinations 615 7$aHumanities 615 7$aSocial interaction 700 $aBerchialla$b Paola$4edt$01325099 702 $aBaldi$b Ileana$4edt 702 $aBerchialla$b Paola$4oth 702 $aBaldi$b Ileana$4oth 906 $aBOOK 912 $a9910557608103321 996 $aBayesian Design in Clinical Trials$93036577 997 $aUNINA