Comparisons of imputation methods with application to assess factors associated with self efficacy of physical activity in breast cancer survivors. Issue 8 (14th September 2019)

• Record Type: Journal Article
• Authors: Zhang, Yunxi; Kim, Soeun; Lin, Ye; Baum, George; Basen-Engquist, Karen M.; Swartz, Michael D.
• Is Part Of: Communications in statistics. Volume 48:Issue 8(2019)
• Page Start: 2523
• Page End: 2537
• Publication Date: 2019-09-14
• Subjects: Missing data -- Multiple imputation -- Non-normal -- Quantile imputation; Statistics; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2018.1458132
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: Missing data are commonly encountered in self-reported measurements and questionnaires. It is crucial to treat missing values using appropriate method to avoid bias and reduction of power. Various types of imputation methods exist, but it is not always clear which method is preferred for imputation of data with non-normal variables. In this paper, we compared four imputation methods: mean imputation, quantile imputation, multiple imputation, and quantile regression multiple imputation (QRMI), using both simulated and real data investigating factors affecting self-efficacy in breast cancer survivors. The results displayed an advantage of using multiple imputation, especially QRMI when data are not normal.