Fused mean structure learning in data integration with dependence

TL;DR

This paper introduces a novel data fusion method for integrating multiple studies with dependent outcomes, improving flexibility, statistical accuracy, and computational efficiency, especially in neuroimaging data analysis.

Contribution

It proposes a new quadratic inference-based fusion approach with a pairwise penalty, along with theoretical properties and an efficient meta-estimator for data integration.

Findings

Enhanced flexibility and performance over existing methods

Theoretical guarantees for the estimator's properties

Successful application to neuroimaging data with an available R package

Abstract

Motivated by image-on-scalar regression with data aggregated across multiple sites, we consider a setting in which multiple independent studies each collect multiple dependent vector outcomes, with potential mean model parameter homogeneity between studies and outcome vectors. To determine the validity of jointly analyzing these data sources, we must learn which of these data sources share mean model parameters. We propose a new model fusion approach that delivers improved flexibility, statistical performance and computational speed over existing methods. Our proposed approach specifies a quadratic inference function within each data source and fuses mean model parameter vectors in their entirety based on a new formulation of a pairwise fusion penalty. We establish theoretical properties of our estimator and propose an asymptotically equivalent weighted oracle meta-estimator that is more computationally efficient. Simulations and application to the ABIDE neuroimaging consortium highlight the flexibility of the proposed approach. An R package is provided for ease of implementation.