Abstract Number:
1191
Submission Type:
Invited Paper Session
Participants:
Yichuan Zhao (1), Yichuan Zhao (1), Haoda Fu (2), Wenbin Lu (3), Yuanjia Wang (4), Donglin Zeng (5)
Institutions:
(1) Georgia State University, N/A, (2) Eli Lilly and Company, N/A, (3) North Carolina State University, N/A, (4) Columbia University, N/A, (5) University of North Carolina, N/A
Chair:
Session Organizer:
Speaker(s):
Session Description:
Recent advancements in technology have opened up new avenues for delivering behavioral interventions to encourage activities like physical exercise. One of the key challenges in personalized medicine lies in estimating the effectiveness of a specific treatment strategy using historical data collected from potentially different treatment approaches. There is a growing demand for individualized modeling and personalized predictions, with applications to healthcare. Mental disorders pose unique challenges in terms of diagnosis and treatment. There is a need to enhance the efficiency of estimating personalized treatment strategies using external data sources. The field of precision medicine revolves around addressing statistical challenges within the realm of health sciences, offering a driving force with broad applications in biomedical research. The remarkable growth in computational power has opened up new horizons, enabling the development of innovative methods in harnessing the potential of data-driven approaches for precision medicine.
By bringing together the leading experts from this area, the session will create an atmosphere for discussing the state-of-the-art research and identifying new research problems in this active and hot research field. In the evolving landscape of precision medicine, several open questions remain pivotal in guiding its future trajectory. The invited speaker in the session will address these questions, highlight the technological innovations, method development that have emerged. Emphasis is placed on the challenges of patient-specific interventions, stabilizing the solutions. In addition, the talk will describe open problems in this area including using reverse reinforcement learning for physician and patient preference. It is essential to learn representative features for precision medicine based on unstructured data, and additional challenges including patient privacy. The speaker in the session will consider off-policy evaluation (OPE) in infinite horizon and irregular observation settings. The talk will develop a new framework for OPE in a relatively general setting, and construct confidence intervals for a given policy's value with reinforcement learning techniques. Electroencephalogram (EEG) has shown promise as a potential biomarker of mental illness. The speaker will propose a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects.
This session focuses on complex data analysis with new applications in personalized medicine. All the speakers in this session are the most accomplished researchers in the world and are also excellent communicators. Their unique insights will be valuable not only to the statisticians working on cutting-edge problems but also to the applied statisticians working in real problems with broad application in biostatistics.
The four speakers and the titles:
Haoda Fu, Eli Lilly and Company. Recent development in open questions for precision medicine,
Wenbin Lu, NCSU. Off-policy evaluation with irregular, outcome-dependent observation process,
Yuanjia Wang, Columbia Univ. A hierarchical state-space model for identifying brain biomarkers for treatment effect heterogeneity,
Donglin Zeng. Univ. of Michigan. Improve the efficiency of individual treatment rule estimation using external data.
Sponsors:
Biometrics Section 1
Biopharmaceutical Section 3
Health Policy Statistics Section 2
Theme:
Statistics and Data Science: Informing Policy and Countering Misinformation
Yes
Applied
Yes
Estimated Audience Size
Medium (80-150)
I have read and understand that JSM participants must abide by the Participant Guidelines.
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I understand and have communicated to my proposed speakers that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is nonrefundable.
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