Patient Recruitment Using Electronic Health Records Under Selection Bias: a Two-phase Sampling Framework

TL;DR

This paper introduces an optimal two-phase sampling framework utilizing electronic health records to improve patient recruitment efficiency for clinical studies, while addressing selection bias inherent in EHR data.

Contribution

It develops a novel two-phase sampling method that accounts for selection bias and leverages auxiliary covariates in EHR data to enhance study efficiency.

Findings

Efficiency gains demonstrated through simulation studies.

Application to hypertension prevalence estimation in US adults.

Method effectively adjusts for EHR selection bias.

Abstract

Electronic health records (EHRs) are increasingly recognized as a cost-effective resource for patient recruitment in clinical research. However, how to optimally select a cohort from millions of individuals to answer a scientific question of interest remains unclear. Consider a study to estimate the mean or mean difference of an expensive outcome. Inexpensive auxiliary covariates predictive of the outcome may often be available in patients' health records, presenting an opportunity to recruit patients selectively which may improve efficiency in downstream analyses. In this paper, we propose a two-phase sampling design that leverages available information on auxiliary covariates in EHR data. A key challenge in using EHR data for multi-phase sampling is the potential selection bias, because EHR data are not necessarily representative of the target population. Extending existing literature on two-phase sampling design, we derive an optimal two-phase sampling method that improves efficiency over random sampling while accounting for the potential selection bias in EHR data. We demonstrate the efficiency gain from our sampling design via simulation studies and an application to evaluating the prevalence of hypertension among US adults leveraging data from the Michigan Genomics Initiative, a longitudinal biorepository in Michigan Medicine.