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The University of Tennessee Health Science Center
09/12/2024 | News release | Distributed by Public on 09/12/2024 09:31
Department of Prevention Medicine Biostatistics Seminar Series: Shape Mediation Analysis in Alzheimer’s Disease Studies
The Division of Biostatistics of the Department of Preventive Medicine, UTHSC, invites you to attend the following seminar.
Time: Monday, September 16, 2:00 PM-3:00 PM CT
LOCATION: 4th Floor Conference Room 400 in the Doctors Office Building at 66 N. Pauline Street, Memphis, TN 38105. Please park in the multi-story parking garage adjacent to the Doctors Office Building, and bring your parking ticket with you so we can validate it.
ZOOM Virtual Room Connection:Register in advance for this meeting to get the Zoom Link
Seminar Website: https://www.eventcreate.com/e/biostatisticsseminar
Shape Mediation Analysis in Alzheimer's Disease Studies
Miyeon Yeon, Ph.D.
Postdoctoral Scholar,
Department of Preventive Medicine ,
University of Tennessee Health Science Center
Mediation analysis has been widely adopted to elucidate the role of intermediary variables derived from neuroimaging data. Structural equation models (SEMs) are typically employed to investigate the influences of exposures on outcomes, with model coefficients being interpreted as causal effects. While existing SEMs are effective tools, limited research has considered shape mediators. In addition, the linear assumption may lead to efficiency losses and decreased predictive accuracy in real-world applications. To address these challenges, we introduce a novel framework for shape mediation analysis, designed to explore the causal relationships between genetic exposures and clinical outcomes, whether mediated or unmediated by shape-related factors while accounting for potential confounding variables. We propose a two-layer shape regression model to characterize the relationships among neurocognitive outcomes, elastic shape mediators, genetic exposures, and clinical confounders. Both simulated studies and real-data analyses demonstrate the superior performance of our proposed method in terms of estimation accuracy and robustness when compared to existing approaches.