Efficient Modeling of Surrogates to Improve Multi-source High-dimensional Integrative Regression

Surrogate variables play an important role in various fields due to the scarcity or absence of gold standard labels.

We develop a novel approach named SASH for Surrogate-Assisted and data-Shielding High-dimensional integrative regression.

It is a semi-supervised approach that efficiently leverages sizable unlabeled samples with error-prone surrogate outcomes from multiple local sites, to improve the learning accuracy of the small gold-labeled data.

To facilitate stable and efficient knowledge extraction from the surrogates, our method first obtains a preliminary supervised estimator, and then uses it to assist training a regularized single index model (SIM) for the surrogates.

Interestingly, through a chain of convex and properly penalized sparse regressions that approximate the SIM loss with bias-correction, our method avoids the local minima issue of the SIM, and fully eliminates the impact of the preliminary estimator's excessive error.

In addition, it protects individual-level information through the aggregation of summary statistics from local sites, leveraging a similar idea of bias-corrected approximation.

Through simulation studies, we demonstrate that our method outperforms existing approaches.

Finally, we apply our method to develop a genetic risk model for type II diabetes using large-scale data sets from UK and Mass General Brigham biobanks, where only a small fraction of subjects in one site are labeled through chart reviewing.