Abstract
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.
Document Type
Article
Publication Date
8-2023
Digital Object Identifier (DOI)
https://doi.org/10.1177/09622802231192947
Funding Information
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially supported by the National Institutes of Health (1R03CA179661-01A1, 5P20GM103436-15) and the Biostatistics and Bioinformatics Shared Resource Facility of the University of Kentucky Markey Cancer Center (P30CA177558). The prostate cancer biomarker study was supported by the National Cancer Institute (5R01CA143428).
Repository Citation
Li, Yuntong; Shelton, Brent J.; St Clair, William; Weiss, Heidi L.; Villano, John L.; Stromberg, Arnold; Wang, Chi; and Chen, Li, "Weighted mean difference statistics for paired data in the presence of missing values" (2023). Markey Cancer Center Faculty Publications. 414.
https://uknowledge.uky.edu/markey_facpub/414
Included in
Epidemiology Commons, Health Information Technology Commons, Oncology Commons, Statistics and Probability Commons
Notes/Citation Information
© The Author(s) 2023