Doctoral Dissertation Research in DRMS: Optimizing Resource Allocation Decisions in Clinical Trials

在今天使用的所有医学干预措施中，只有不到一半得到了临床证据的支持。尽管联邦政府每年拨款超过110亿美元支持临床研究，但联邦政府对医学研究的资助缺乏一个协调的、量化的资源分配系统，以有效地解决这些重要的知识差距。这项研究将直接解决在信息价值分析和风险预测方面的有希望的进展是否可以用来改善公共资助的癌症临床试验合作小组的决策。该项目将开发、测试和验证快速估计临床试验提案的预期风险和回报的过程，并使用这些估计来探索投资组合优化框架如何改善有限公共资源的分配。在更广泛的影响方面，这项研究将为与公共资助的临床试验信息的风险和回报相关的因素提供新的见解，这些因素可以直接有助于改进临床研究的设计和促进有限研究资金的有效和系统分配的政策。此外，这项工作的发现将推动开发一个协调系统以优先进行临床研究的努力，这是将有限的公共资金用于可能对公共健康产生最大影响的方式的关键一步。

Fewer than half of all medical interventions in use today are supported by clinical evidence. Despite allocating more than $11 billion each year to support clinical research, the federal government's funding of medical research lacks a coordinated, quantitative system for allocating resources to efficiently address these important knowledge gaps. This research will directly address whether or not promising advances in value-of-information analyses and risk prediction can be used to improve decision-making within a publicly funded cancer clinical trials co-operative group. This project will develop, test, and validate processes for rapidly estimating the expected risk and return for clinical trials proposals, and using these estimates explore how a portfolio optimization framework could improve allocations of limited public resources. In terms of broader impacts, this research will provide new insights into factors associated with risk and return of publicly funded clinical trials' information that can directly contribute to the improved design of clinical research and policies that advance the efficient and systematic allocation of limited research funds. Furthermore, the findings from this work will advance efforts to develop a coordinated system to prioritize clinical research, a critical step to direct limited public funds in ways that are likely to have the greatest impact on public health.