Given the time- and resource-intense nature of human subjects research, we have developed a more intelligent approach to participant recruitment above and beyond random sampling that leverages pilot or preliminary results to reduce the overall number of participants needed for recruitment from an existing electronic cohort or database. Using open-access data from the General Social Survey (GSS) of the National Opinion Research Center, we generated pilot and validation datasets through a simulation to establish moderate and weak relationships based on linear regression. We then compared the performance of our residual-matching method against random sampling in their probabilities of achieving a given level of statistical power as well as their prediction accuracies. Results showed that the residual-matching method was superior to random sampling, yielding smaller sample sizes with equivalent mean square error. We therefore advocate the use of residual matching when scaling up pilot studies to conserve time and resources in larger follow-up studies.

中文翻译：

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残差匹配：人类受试者研究募集中随机抽样的有效替代方案

考虑到人类受试者研究的时间和资源密集性，我们已经开发了一种更智能的参与者招募方法，超越了随机抽样，它利用试点或初步结果来减少从现有电子队列招募所需的参与者总数或数据库。使用来自国家舆论研究中心的一般社会调查（GSS）的开放获取数据，我们通过模拟生成了试验和验证数据集，以基于线性回归建立中度和弱度关系。然后，我们比较了残差匹配方法与随机抽样的性能，以达到给定水平的统计能力以及预测准确性。结果表明，残差匹配方法优于随机抽样，产生较小的样本量，等效均方误差。因此，我们提倡在扩大试点研究规模时使用残差匹配，以节省大量后续研究的时间和资源。