A1846
Title: Generalized heterogeneous functional model with applications to large-scale mobile health data
Authors: Fei Xue - Purdue University (United States) [presenting]
Abstract: Physical activity is crucial for human health. 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. A generalized heterogeneous functional method is proposed that 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 is developed that enhances computational efficiency for large-scale data through finer partitioning of subjects compared to true subgroups. A testing procedure is introduced to assess whether different subgroups exhibit distinct functional effects. In a real data application examining the impact of physical activity on the risk of dementia using the UK Biobank dataset with over 96,433 participants, the proposed method outperforms existing methods in future-day prediction accuracy, identifying three distinct subgroups with detailed scientific interpretations for each. The theoretical consistency of the methods is also demonstrated.
