A1047
Title: Generalized heterogeneous functional model with applications to large-scale mobile health data
Authors: Xiaojing Sun - Purdue University (United States)
Bingxin Zhao - University of Pennsylvania (United States)
Fei Xue - Purdue University (United States) [presenting]
Abstract: With the increasing availability of large-scale mobile health data, strong associations have been found between physical activity and various diseases. However, accurately capturing this complex relationship is challenging, possibly because it varies across different subgroups of subjects, especially in large-scale datasets. To fill this gap, a generalized heterogeneous functional method is proposed, which simultaneously estimates functional effects and identifies subgroups within the generalized functional regression framework. The proposed method captures subgroup-specific functional relationships between physical activity and diseases, providing a more nuanced understanding of these associations. Additionally, a pre-clustering method that enhances computational efficiency for large-scale data through a finer partition of subjects compared to true subgroups is introduced. A testing procedure is further developed to assess whether the identified subgroups exhibit truly distinct functional effects and whether heterogeneity exists across the entire population. In the real data application, the impact of physical activity is examined on the risk of mental disorders and Parkinson's disease using the UK Biobank dataset, which includes over 79,000 participants. The proposed method outperforms existing methods in future-day prediction accuracy, identifying four subgroups for mental disorder outcomes and three subgroups for Parkinson's disease diagnosis.
