Author(s)

Michikazu Nakai, Ding-Geng Chen, Kunihiro Nishimura, Yoshihiro Miyamoto

chool of Nursing & Department of Biostatistics and Computational Biology, School of Medicine, University of Rochester Medical Center, New York, USA.
Department of Preventive Medicine and Epidemiologic Informatics, National Cerebral and Cardiovascular Center, Suita, Japan.

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.

Missing Data; Imputation; MCAR; Complete Case; LOCF

M. Nakai, D. Chen, K. Nishimura and Y. Miyamoto, "Comparative Study of Four Methods in Missing Value Imputations under Missing Completely at Random Mechanism," Open Journal of Statistics, Vol. 4 No. 1, 2014, pp. 27-37. doi: 10.4236/ojs.2014.41004.