MARKOV PROCESSES AND CLINICAL TRIAL DESIGN

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 子项目的主要研究者可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 癌症等严重疾病的早期临床试验在平衡患者的风险和收益与社区的成本和收益方面提出了严峻的挑战，特别是要获得的科学信息的价值。原则上，决策理论为这个问题提供了一个清晰合理的解决方案。然而，评估未来知识的价值涉及组合爆炸性计算。这项工作适应和评估几种近似方法，包括一些相当新的技术开发的后期临床试验，也从规划和机器人技术解决部分可观察马尔可夫决策过程

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Early stage clinical trials for serious diseases like cancer pose serious challenges in balancing the risks and benefits for the patient enrolled with the costs and benefits to the community, especially the value of the scientific information to be gained. In principle, decision theory provides a clear rational solution to the problem. However, evaluating the value of future knowledge to be gained involves combinatorially explosive calculations. This work adapts and evaluates several approximation methods, including some fairly recent techniques developed for later-stage clinical trials, and also techniques from planning and robotics for solving partially observable Markov decision processes