The analysis of plasma microRNAs (miRNAs) has been widely used as a method for finding potential biomarkers for human diseases, especially those with a link to cancer. Methods of analyzing plasma miRNA have been thoroughly discussed from sample extraction to data modeling. However, some issues exist within the process that have rarely been talked about. Rice et al. discussed some issues in plasma miRNA studies, such as the lack of standard methodology including the use of different cycle threshold, time to plasma extraction, among others. These issues can lead to inconsistent data, and thus impact the result and assay reproducibility. Other external issues, such as batch effect and operator effect, may also indirectly impact the statistical analysis. Here, we discuss issues in plasma miRNA studies from a statistical point of view. The interaction effect of different ways of calculating fold-change, the choice of housekeeping genes, and methods of normalization are among the issues we discuss, with data demonstrations. P values are calculated and compared to determine the effect of those issues on statistical conclusions. Statistical methods such as analysis of variance and analysis of covariance are crucial in the analysis of miRNA but investigators are often confused about them; therefore, a brief explanation of these statistical methods is also included. In addition, 3-group classification is discussed, as it is often challenging, compared with 2-group classification.

血浆microRNA（miRNA）的分析已广泛用作寻找人类疾病（尤其是与癌症有关的疾病）潜在生物标志物的方法。从样品提取到数据建模，已经详细讨论了分析血浆miRNA的方法。但是，过程中存在一些鲜为人知的问题。赖斯等。讨论了血浆miRNA研究中的一些问题，例如缺乏标准方法，包括使用不同的循环阈值，提取血浆的时间等。这些问题可能导致数据不一致，从而影响结果和测定的可重复性。其他外部问题，例如批次效应和操作者效应，也可能间接影响统计分析。在这里，我们从统计学的角度讨论血浆miRNA研究中的问题。计算P值并进行比较，以确定这些问题对统计结论的影响。诸如方差分析和协方差分析之类的统计方法对于miRNA的分析至关重要，但研究人员对此常常感到困惑。因此，还包括对这些统计方法的简要说明。此外，讨论了3组分类，因为与2组分类相比通常具有挑战性。