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 article, 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.

中文翻译：

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使用电子健康记录改善观察性研究的选择性招募设计。

大规模电子健康记录 (EHR) 提供了一个快速识别合适个体以直接邀请他们参与观察性研究的机会。EHR 可以包含来自数百万个人的数据，这引发了如何从更大的大小为N的池中最佳选择大小为n的队列的问题. 在本文中，我们提出了一个简单的选择性招募协议，该协议选择了一个感兴趣的协变量倾向于均匀分布的队列。我们表明，与随机选择的群组相比，选择性招募的群组可能提供更大的统计能力和更准确的参数估计。我们的协议可以应用于具有多个分类和连续协变量的研究。我们将我们的协议应用于数值模拟的前瞻性观察研究，该研究使用来自英国 82 089 人的稳定急性冠状动脉疾病患者的 EHR 数据库选择性招募设计需要更小的样本量，从而实现更高效和更具成本效益的研究。