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 paper, 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 for 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.

翻译：缺少数据是许多生物医学研究中常见的问题。在配对式设计中,有些对象可能由于跟踪缺失、生物样本不足等原因而在一种或两种条件下都缺少价值。这种部分配对的数据使两个条件之间利益变量分布的统计比较复杂化。在本文中,我们根据加权抽样手段的差异提出一般的测试统计类别,而不强加任何分配或模型假设。为这一类试验得出了最佳加权。模拟研究表明,我们提议的最佳重量测试在实际情况下效果良好,优于现有方法。提供了两份癌症生物标记研究作为示例。