MARKOV PROCESSES AND CLINICAL TRIAL DESIGN

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。所列机构为 中心，不一定是研究者的机构。 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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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