在许多生物医学研究中，缺失数据是一个常见问题。在成对设计中，由于失去随访、生物样本不足等原因，一些受试者可能在一个或两个条件中都有缺失值。这种部分成对数据使得对比两个条件之间感兴趣变量的分布的统计比较变得复杂。在本文中，我们提出了一种基于加权样本均值差异的一般类别的检验统计量，不对分布或模型做任何假设。从这一类检验中得出了最佳权重。模拟研究表明，我们提出的具有最佳权重的检验在实际情况下表现良好，并且优于现有方法。为了说明，我们提供了两个癌症生物标志物研究的案例。

Missing data is a common issue in many biomedical studies. Under a paired design, some subjects may have missing values in either one or both of the conditions due to loss of follow-up, insufficient biological samples, etc. Such partially paired data complicate statistical comparison of the distribution of the variable of interest between the two conditions. In this article, we propose a general class of test statistics based on the difference in weighted sample means without imposing any distributional or model assumption. An optimal weight is derived from this class of tests. Simulation studies show that our proposed test with the optimal weight performs well and outperforms existing methods in practical situations. Two cancer biomarker studies are provided for illustration.