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Missing data imputation: focusing on single imputation

	author = {Zhongheng Zhang},
	title = {Missing data imputation: focusing on single imputation},
	journal = {Annals of Translational Medicine},
	volume = {4},
	number = {1},
	year = {2016},
	keywords = {},
	abstract = {Complete case analysis is widely used for handling missing data, and it is the default method in many statistical packages. However, this method may introduce bias and some useful information will be omitted from analysis. Therefore, many imputation methods are developed to make gap end. The present article focuses on single imputation. Imputations with mean, median and mode are simple but, like complete case analysis, can introduce bias on mean and deviation. Furthermore, they ignore relationship with other variables. Regression imputation can preserve relationship between missing values and other variables. There are many sophisticated methods exist to handle missing values in longitudinal data. This article focuses primarily on how to implement R code to perform single imputation, while avoiding complex mathematical calculations.},
	issn = {2305-5847},	url = {}