TITLE:
Comparative Study of Four Methods in Missing Value Imputations under Missing Completely at Random Mechanism
AUTHORS:
Michikazu Nakai, Ding-Geng Chen, Kunihiro Nishimura, Yoshihiro Miyamoto
KEYWORDS:
Missing Data; Imputation; MCAR; Complete Case; LOCF
JOURNAL NAME:
Open Journal of Statistics,
Vol.4 No.1,
January
27,
2014
ABSTRACT:
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.