Comparison of Four Methods for Handing Missing Data in Longitudinal Data Analysis through a Simulation Study
Read full paper at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=52855#.VKs-f8nQrzE Author(s) Xiaoping Zhu Affiliation(s) Biostatistics & Data Management, Regeneron Pharmaceuticals, Inc., Basking Ridge, USA . ABSTRACT Missing data can frequently occur in a longitudinal data analysis. In the literature, many methods have been proposed to handle such an issue. Complete case (CC), mean substitution (MS), last observation carried forward (LOCF), and multiple imputation (MI) are the four most frequently used methods in practice. In a real-world data analysis, the missing data can be MCAR, MAR, or MNAR depending on the reasons that lead to data missing. In this paper, simulation...