PPV-guided cost-effective chart review for model-agnostic RWD-based discovery

Oregon Convention Center 

Electronic Health Record (EHR)-based association studies have been commonly used to identify the risk factors associated with patient clinical phenotypes. While EHR-derived phenotypes (i.e., surrogates) have recently been utilized, manual chart reviews remain the gold standard for ensuring the quality of the phenotypes. This process is notably time-consuming and costly. Therefore, determining the optimal subset size for chart review is of great importance. In this paper, we propose a PPV/NPV-guided method to determine the minimum sample size required for chart review, thereby substantially saving the cost. Subsequently, we introduce an augmented estimation procedure that effectively combines the chart reviews with the surrogates to achieve asymptotically unbiased and efficient estimators for the EHR-based association studies. Our approach offers a cost-effective solution that ensures accuracy and efficiency in estimation without explicitly specifying the misclassification mechanism of the surrogates. The robustness of our method is validated through extensive simulation studies and the evaluation of real-world data, utilizing the Flatiron dataset as a benchmark for verification.

Electronic Health Record (EHR)

Association study

Outcome dependent sampling 

Section on Statistics in Epidemiology