Comparative Study of Four Methods in Missing Value Imputations under Missing Completely at Random Mechanism

• 作者: Ding-Geng Chen Kunihiro Nishimura Michikazu Nakai Yoshihiro Miyamoto
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: MISSING Data IMPUTATION MCAR COMPLETE Case LOCF
• 年，卷（期）: 2014,（1）
• 页码范围: 27-37
• 页数: 11页
• 分类号: R73

摘要

In analyzing data from clinical trials and longitudinal studies, the issue of missing values is always a fundamental challenge since the missing data could introduce bias and lead to erroneous statistical inferences. To deal with this challenge, several imputation methods have been developed in the literature to handle missing values where the most commonly used are complete case method, mean imputation method, last observation carried forward (LOCF) method, and multiple imputation (MI) method. In this paper, we conduct a simulation study to investigate the efficiency of these four typical imputation methods with longitudinal data setting under missing completely at random (MCAR). We categorize missingness with three cases from a lower percentage of 5% to a higher percentage of 30% and 50% missingness. With this simulation study, we make a conclusion that LOCF method has more bias than the other three methods in most situations. MI method has the least bias with the best coverage probability. Thus, we conclude that MI method is the most effective imputation method in our MCAR simulation study.