Batch effects are pervasive in biomedical studies. One approach to address the batch effects is repeatedly measuring a subset of samples in each batch. These remeasured samples are used to estimate and correct the batch effects. However, rigorous statistical methods for batch effect correction with remeasured samples are severely under-developed. In this study, we developed a framework for batch effect correction using remeasured samples in highly confounded case-control studies. We provided theoretical analyses of the proposed procedure, evaluated its power characteristics, and provided a power calculation tool to aid in the study design. We found that the number of samples that need to be remeasured depends strongly on the between-batch correlation. When the correlation is high, remeasuring a small subset of samples is possible to rescue most of the power.

翻译：批次效应在生物医学研究中普遍存在。解决批次效应的一种方法是对每个批次中的部分样本进行重复测量，利用这些重复测量样本估计并校正批次效应。然而，基于重复测量样本进行批次效应校正的严谨统计方法仍严重缺乏。在本研究中，我们针对高度混杂的病例对照研究，提出了一种利用重复测量样本进行批次效应校正的框架。我们对所提出的程序进行了理论分析，评估了其统计功效特性，并提供了辅助研究设计的功效计算工具。研究发现，需要重复测量的样本数量强烈依赖于批次间相关性。当相关性较高时，仅需重复测量少量样本即可挽救大部分统计功效。