A new information integration framework for complex models with applications to real-world data

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 model that allows certain covariate effects to vary with the values of another covariate. This model is widely used in biomedical research, particularly in scenarios where assuming linear effects across all covariates is inappropriate. This article addresses this gap by introducing a new information integration framework. This new framework (a) enables computationally efficient integration of information from a different external model type (e.g. generalized linear models), (b) does not assume homogeneous data distributions across sites or studies, and (c) 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 to identify (a) risk factors influencing blood pressure using data from the UK Biobank and ARIC studies, and (b) risk factors associated with prolonged hospital length of stay among older fracture patients with ADRD using Medicare claims.