# Selective recruitment designs for improving observational studies using electronic health records

## TL;DR

This paper introduces a selective recruitment protocol for observational studies using EHR data, which improves statistical power and reduces sample size by ensuring covariate uniformity in the recruited cohort.

## Contribution

The paper proposes a simple, adaptable recruitment method that enhances study efficiency and accuracy by optimizing covariate distribution in the selected cohort.

## Key findings

- Selective recruitment yields higher statistical power.
- Smaller sample sizes are sufficient with the proposed protocol.
- Method is applicable to multiple covariate types.

## Abstract

Large scale electronic health records (EHRs) present an opportunity to quickly identify suitable individuals in order to directly invite them to participate in an observational study. EHRs can contain data from millions of individuals, raising the question of how to optimally select a cohort of size n from a larger pool of size N. In this paper we propose a simple selective recruitment protocol that selects a cohort in which covariates of interest tend to have a uniform distribution. We show that selectively recruited cohorts potentially offer greater statistical power and more accurate parameter estimates than randomly selected cohorts. Our protocol can be applied to studies with multiple categorical and continuous covariates. We apply our protocol to a numerically simulated prospective observational study using an EHR database of stable acute coronary disease patients from 82,089 individuals in the U.K. Selective recruitment designs require a smaller sample size, leading to more efficient and cost-effective studies.