Surrogate-powered Regularized Estimation: Semi-Supervised Modeling with Multi-Wave Sampling

Music City Center 

Surrogate-powered modeling is an emerging approach in semi-supervised learning that improves statistical efficiency by integrating large-scale unlabeled data with a small labeled dataset using multiple surrogate outcomes. This framework is particularly useful in risk modeling with electronic health records (EHR), where gold-standard outcomes are limited due to costly chart reviews, while algorithm-generated surrogates are widely available. Key challenges include effectively combining labeled and unlabeled data with multiple surrogates and designing efficient sampling rules for chart reviews. To address these, we propose a multi-wave sampling strategy to adaptively approximate the optimal sampling rule and introduce a novel semi-supervised estimator with first-order bias correction and sparse regularization to reduce estimation errors. The estimator is asymptotically normal, unbiased, and improves statistical efficiency. Extensive numerical studies demonstrate its effectiveness in reducing mean-squared error.

EHR data

semi-supervised learning

surrogate regression

bias-reduction 

Section on Statistics in Epidemiology