Collaborative Research: Integrative Heterogeneous Learning for Intensive Complex Longitudinal Data

点击翻译按钮获取中文摘要

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.