000724746 000__ 03311cam\a2200517Ii\4500 000724746 001__ 724746 000724746 005__ 20230306140549.0 000724746 006__ m\\\\\o\\d\\\\\\\\ 000724746 007__ cr\cn\nnnunnun 000724746 008__ 141209s2015\\\\gw\a\\\\ob\\\\000\0\eng\d 000724746 019__ $$a903242932$$a908087507 000724746 020__ $$a9783658083441$$qelectronic book 000724746 020__ $$a3658083441$$qelectronic book 000724746 020__ $$z9783658083434 000724746 0247_ $$a10.1007/978-3-658-08344-1$$2doi 000724746 035__ $$aSP(OCoLC)ocn897810267 000724746 035__ $$aSP(OCoLC)897810267$$z(OCoLC)903242932$$z(OCoLC)908087507 000724746 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dTEF$$dOCLCO$$dN$T$$dGW5XE$$dCOO$$dOCLCF$$dVLB$$dIDEBK$$dYDXCP$$dCDX$$dEBLCP$$dOCLCO 000724746 0411_ $$aeng$$beng$$bger$$heng 000724746 049__ $$aISEA 000724746 050_4 $$aR853.S7$$bO5 2015eb 000724746 08204 $$a610.72/4$$223 000724746 08204 $$a510 000724746 1001_ $$aOn, Thomas,$$eauthor. 000724746 24510 $$aOptimized response-adaptive clinical trials$$h[electronic resource] :$$bsequential treatment allocation based on Markov decision problems /$$cThomas Ondra. 000724746 264_1 $$aWiesbaden :$$bSpringer Spektrum,$$c2015. 000724746 300__ $$a1 online resource (xv, 102 pages) :$$billustrations. 000724746 336__ $$atext$$btxt$$2rdacontent 000724746 337__ $$acomputer$$bc$$2rdamedia 000724746 338__ $$aonline resource$$bcr$$2rdacarrier 000724746 347__ $$atext file$$bPDF$$2rda 000724746 4901_ $$aBestMasters 000724746 504__ $$aIncludes bibliographical references. 000724746 5050_ $$aIntroduction to Markov Decision Problems and Examples -- Finite and Infinite Horizon Markov Decision Problems -- Solution Algorithms: Backward Induction, Value Iteration and Policy Iteration -- Designing Response Adaptive Clinical Trials with Markov Decision Problems. 000724746 506__ $$aAccess limited to authorized users. 000724746 520__ $$aTwo-armed response-adaptive clinical trials are modelled as Markov decision problems to pursue two overriding objectives: Firstly, to identify the superior treatment at the end of the trial and, secondly, to keep the number of patients receiving the inferior treatment small. Such clinical trial designs are very important, especially for rare diseases. Thomas Ondra presents the main solution techniques for Markov decision problems and provides a detailed description how to obtain optimal allocation sequences. Contents Introduction to Markov Decision Problems and Examples Finite and Infinite Horizon Markov Decision Problems Solution Algorithms: Backward Induction, Value Iteration and Policy Iteration Designing Response Adaptive Clinical Trials with Markov Decision Problems Target Groups Researchers and students in the fields of mathematics and statistics Professionals in the pharmaceutical industry The Author Thomas Ondra obtained his Master of Science degree in mathematics at University of Vienna. He is a research assistant and PhD student at the Section for Medical Statistics of Medical University of Vienna. 000724746 546__ $$aAbstract in German and English 000724746 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed Feb. 26, 2015). 000724746 650_0 $$aClinical trials$$xStatistical methods. 000724746 650_0 $$aMarkov processes. 000724746 77608 $$iPrint version:$$z9783658083434 000724746 830_0 $$aBestMasters. 000724746 852__ $$bebk 000724746 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-658-08344-1$$zOnline Access$$91397441.1 000724746 909CO $$ooai:library.usi.edu:724746$$pGLOBAL_SET 000724746 980__ $$aEBOOK 000724746 980__ $$aBIB 000724746 982__ $$aEbook 000724746 983__ $$aOnline 000724746 994__ $$a92$$bISE