@article{724746,
      recid = {724746},
      author = {On, Thomas,},
      title = {Optimized response-adaptive clinical trials sequential  treatment allocation based on Markov decision problems /  [electronic resource] :},
      pages = {1 online resource (xv, 102 pages) :},
      abstract = {Two-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.},
      url = {http://library.usi.edu/record/724746},
      doi = {https://doi.org/10.1007/978-3-658-08344-1},
}