Collaborative Research: Integrative Heterogeneous Learning for Intensive Complex Longitudinal Data

这个项目解决了几个与生物医学信息相关的基本统计问题。这些问题出现在复杂的生物医学研究中，当数据呈现高度异质性和决策阶段数很大时。研究人员的目标是开发新的统计方法和有效的计算工具，以解决实际应用，如创伤后应激障碍(PTSD)的治疗和移动健康数据在糖尿病管理中的使用。这项研究有望在卫生保健方面有用，并促进神经科学、心理健康、护理、传染病、表观遗传学、生物学和计算机科学的跨学科研究。将开发的软件将广泛传播，供业界合作伙伴使用。该项目通过参与研究为研究生提供培训。调查人员将研究三个研究课题。第一个动机是确定创伤后应激障碍患者的异质性表观遗传效应。这项研究将通过开发一种新的模型来识别高维DNA甲基化介体，从而突破目前的界限。第二个主题是受移动医疗技术最新进展的推动，移动医疗技术有效地监测个人的健康状况并提供个性化治疗。“价值提升”的概念被用来选择最优治疗。当决策阶段的数量可以发散到无穷大时，研究人员将研究一种新的估计器。最佳方案可以是稀疏的和适应性的，如果有许多治疗选择，它就会更有利。它还将对临时药物短缺或预算限制等意外情况具有很强的抵抗力。在第三个主题中，研究人员计划开发一种双编码器方法，通过结合联合治疗的协同或拮抗效应等交互效应来评估最佳全渠道个体化治疗规则。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project addresses several fundamental statistical questions related to biomedical information. These questions arise in complex biomedical studies when the data present high heterogeneity and the number of decision stages is large. The investigators aim to develop new statistical methods and efficient computational tools to address practical applications such as treatment of post-traumatic stress disorder (PTSD) and the use of mobile health data in management of diabetes. The research is expected to be useful in health care and stimulate interdisciplinary research in neuroscience, mental health, nursing, infectious diseases, epigenetics, biology, and computer science. The software to be developed will be widely disseminated for use by industry partners. The project provides training through research involvement for graduate students. The investigators will study three research topics. The first is motivated by identifying heterogeneous epigenetic effects of PTSD patients. This research will push the current boundaries by developing a new model to identify high-dimensional DNA methylation mediators. The second topic is motivated by recent advances in mobile health technology, which effectively monitors individuals' health statuses and delivers personalized treatment. The concept of "value enhancement" is used to select the optimal treatment. The investigators will study a novel estimator when the number of decision stages can diverge to infinity. The optimal regime could be sparse and adaptive, making it more advantageous if there are many treatment options. It will also be robust to unexpected situations such as temporary medication shortages or budget constraints. In the third topic, the investigators plan to develop a double encoders approach to estimate the optimal omni-channel individualized treatment rule by incorporating interaction effects such as synergistic or antagonistic effects due to combination treatments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.