Optimal Surrogate-Assisted Sampling for Cost-Efficient Validation of Electronic Health Record Outcomes.

Electronic Health Record (EHR) databases are an increasingly valuable resource for observational studies. However, misclassification of EHR-derived outcomes due to imperfect phenotyping leads to bias, inflated type I error, and reduced power in risk-factor association studies. On the other hand, manual chart review to validate outcomes is both cost-prohibitive and time-consuming, and a randomly selected validation sample may not yield sufficient cases to support precise model estimation when the disease is rare. Sampling procedures have been developed for maximizing computational and statistical efficiency in settings where the true disease status is known. However, less work has been done in measurement constrained settings, particularly when an informative surrogate outcome is available. Motivated by this gap, we propose an Optimal Subsampling strategy with Surrogate-Assisted Two-step procedure (OSSAT) to guide cost-effective chart review in measurement constrained settings. The sampling weight in OSSAT leverages information contained in the potentially misclassified phenotype and covariates to prioritize observations most informative for the model of interest. We compare our proposed weight with existing approaches through simulations under various covariate distributions, differential misclassification rates and degrees of surrogate accuracy. We then apply our proposed weighting schemes to a study of risk factors for second breast cancer events using a real EHR data set.