55: A New Information Integration Framework in VCM with Applications to Real-World Data

Music City Center 

Over the past few decades, various advanced methods have been developed to facilitate information integration. These methods leverage summary statistics (e.g., point estimates) from multiple sites or studies, which can be readily extracted from existing publications or efficiently shared via correspondence, without requiring the sharing of raw individual-level data. Despite these advancements, existing methods may not be directly applicable to the varying coefficient model (VCM)-a semi-parametric framework that allows certain covariate effects to vary with the values of another covariate. This paper addresses this gap by introducing a comprehensive information integration framework for VCM. This new framework (1) enables computationally efficient integration of information from a different model type (e.g., generalized linear models), (2) does not assume homogeneous data distributions across sites or studies, and (3) supports variable selection. Extensive simulations validate the proposed method, demonstrating substantial variance reduction with minimal estimation bias in various cases. Finally, we apply this method to two distinct datasets.

Empirical likelihood

Information integration

Real-world data

Varying coefficient model

Variable selection 

ENAR