Integrative data analysis where partial covariates have complex non-linear effects by using summary information from an external data

TL;DR

This paper introduces a novel integrative inference framework for partially linear models that efficiently combines external summary data to improve estimation accuracy in complex, non-linear covariate effects.

Contribution

It develops a new statistical method that synthesizes external summary information into PLM, enhancing estimation efficiency and model flexibility.

Findings

The method is theoretically valid and numerically convenient.

It achieves high-efficiency gains over classic PLM methods.

Validated with real data on brain imaging and blood pressure.

Abstract

A full parametric and linear specification may be insufficient to capture complicated patterns in studies exploring complex features, such as those investigating age-related changes in brain functional abilities. Alternatively, a partially linear model (PLM) consisting of both parametric and non-parametric elements may have a better fit. This model has been widely applied in economics, environmental science, and biomedical studies. In this paper, we introduce a novel statistical inference framework that equips PLM with high estimation efficiency by effectively synthesizing summary information from external data into the main analysis. Such an integrative scheme is versatile in assimilating various types of reduced models from the external study. The proposed method is shown to be theoretically valid and numerically convenient, and it ensures a high-efficiency gain compared to classic methods in PLM. Our method is further validated using two data applications by evaluating the risk factors of brain imaging measures and blood pressure.