Accounting for Dependence Induced by Weighted KNN Imputation in Paired Samples,Motivated by a Colorectal Cancer Study |
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Authors: | Anvar Suyundikov John R Stevens Christopher Corcoran Jennifer Herrick Roger K Wolff Martha L Slattery |
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Institution: | 1. Department of Mathematics and Statistics, Utah State University, 3900 Old Main Hill, Logan, UT 84322-3900, U.S.A..; 2. Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, 383 Colorow Road, Salt Lake City, UT 84108, U.S.A..; National Taiwan University, TAIWAN, |
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Abstract: | Missing data can arise in bioinformatics applications for a variety of reasons, and imputation methods are frequently applied to such data. We are motivated by a colorectal cancer study where miRNA expression was measured in paired tumor-normal samples of hundreds of patients, but data for many normal samples were missing due to lack of tissue availability. We compare the precision and power performance of several imputation methods, and draw attention to the statistical dependence induced by K-Nearest Neighbors (KNN) imputation. This imputation-induced dependence has not previously been addressed in the literature. We demonstrate how to account for this dependence, and show through simulation how the choice to ignore or account for this dependence affects both power and type I error rate control. |
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