Partially paired data, either with incompleteness in one or both arms, are common in practice. For testing equality of means of two arms, practitioners often use only the portion of data with complete pairs and perform paired tests. Although such tests (referred as ‘naive paired tests’) are legitimate, their powers might be low as only partial data are utilized. The recently proposed ‘P-value pooling methods’, based on combining P-values from two tests, use all data, have reasonable type-I error control and good power property. While it is generally believed that ‘P-value pooling methods’ are superior to ‘naive paired tests’ in terms of power as the former use more data than the latter, no detailed power comparison has been done. This paper aims to compare powers of ‘naive paired tests’ and ‘P-value pooling methods’ analytically and our findings are counterintuitive, i.e. the ‘P-value pooling methods’ do not always outperform the naive paired tests in terms of power. Based on these results, we present guidance on how to select the best test for testing equality of means with partially paired data.

部分配对的数据，无论是在一个或两个臂中都不完整，在实践中很常见。为了检验两条臂均值的相等性，从业者通常只使用具有完整配对的部分数据并进行配对检验。尽管此类测试（称为“幼稚配对测试”）是合法的，但由于仅使用了部分数据，因此它们的能力可能很低。最近提出的“ P值池化方法”，基于组合来自两个测试的 P 值，使用所有数据，具有合理的 I 型错误控制和良好的功率特性。虽然普遍认为' P值池方法在功效方面优于“朴素配对检验”，因为前者使用的数据多于后者，没有进行详细的功效比较。本文旨在分析比较“朴素配对检验”和“ P值汇集方法”的功效，我们的发现与直觉相反，即“ P值汇集方法”在功效方面并不总是优于朴素配对检验。基于这些结果，我们提供了有关如何选择最佳检验来检验具有部分配对数据的均值性的指导。