Weighted Mean Difference Statistics for Paired Data in Presence of Missing Values

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 distribu...

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Bibliographic Details
Main Authors Li, Yuntong, Shelton, Brent J, Clair, William St, Weiss, Heidi L, Villano, John L, Stromberg, Arnold J, Wang, Chi, Chen, Li
Format Journal Article
LanguageEnglish
Published 24.10.2021
Subjects
Statistics - Methodology
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DOI10.48550/arxiv.2110.12582

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Summary: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.
DOI:10.48550/arxiv.2110.12582