New Methodology and Theory for Optimal Treatment Regimes with Applications to Precision Medicine

根据个体特征寻找最佳治疗方案或一系列顺序治疗方案的问题在精准医学、政府政策和积极的劳动力市场干预等领域具有重要的应用。 根据应用，治疗可以代表药物，设备，程序，政策，干预或策略。 在基因组学和医学成像等领域的进步的刺激下，过去十年在个性化医疗方面取得了令人兴奋和显着的进展，从乳腺癌的治疗到重度抑郁症的治疗。 精准医疗的成功取决于开发准确可靠的统计和机器学习工具，以根据从随机实验或观察性研究中收集的数据估计最佳治疗方案。 该项目将开发新的方法，理论和算法，有可能显着推进最佳治疗方案的统计估计和推断的最新技术水平。 该研究将显著提高静态或动态最佳治疗方案估计的统计方法和理论的可用性。 一个系统的框架，估计最佳的治疗方案，使用一个新的分位数标准的各种情况下，将制定。 该研究将侧重于一阶段（静态）治疗方案和动态治疗方案，后者允许治疗随时间变化。 此外，研究将解决完全观察到的反应和随机审查的反应（例如，生存时间）、随机试验和观察性研究以及双重稳健估计。该框架还将扩展到其他标准，如基尼平均差。该项目将显着推进最佳治疗方案的一大类鲁棒估计的理论基础。 此外，它解决了具有挑战性的和重要的问题，开发新的方法和算法，以确定重要的变量，在高维设置的最佳治疗方案估计。 调查员将开发软件包，免费提供给研究界。 将特别鼓励少数群体的学生参加拟议的项目。

The problem of finding the optimal treatment regime, or a series of sequential treatment regimes, based on individual characteristics has important applications in areas such as precision medicine, government policies, and active labor market interventions. Depending on the application, a treatment can represent a drug, a device, a program, a policy, an intervention, or a strategy. Stimulated by the advancements in fields such as genomics and medical imaging, the last decade has witnessed exciting and remarkable progress in personalized medicine, ranging from treatments for breast cancer to treatments for major depressive disorders. The success of precision medicine depends on the development of accurate and reliable statistical and machine learning tools for estimating the optimal treatment regime given the data collected from randomized experiments or observational studies. This project will develop novel methodology, theory, and algorithms with the potential to significantly advance the state of the art in statistical estimation and inference for optimal treatment regimes. The proposed research will significantly enhance the availability of statistical methodology and theory for static or dynamic optimal treatment regimes estimation. A systematic framework for estimating optimal treatment regimes using a new quantile criterion for a variety of scenarios will be developed. The research will focus on both one-stage (static) treatment regimes and dynamic treatment regimes, the latter allowing for treatments to vary with time. In addition, the research will address completely observed responses and randomly censored responses (e.g., survival times), randomized trials and observational studies, and doubly robust estimation. The framework will also be extended to alternative criteria such as Gini's mean difference. This project will significantly advance the theoretical foundations of a large class of robust estimators of optimal treatment regimes. Furthermore, it addresses the challenging and important problem of developing new methodology and algorithms to identity important variables for optimal treatment regime estimation in the high-dimensional setting. The investigator will develop software packages and make them freely available to the research community. Students from minority groups will be especially encouraged to participate in the proposed project.