Comparison of Multiple Imputation and Complete-Case in a Simulated Longitudinal Data with Missing Covariate

Along a continual process of collecting data, missing recorded datum always a main problem faced by the real application. It happens due to the carelessness or the unawareness of a recorder to the importance of data documentation. In this study, a random-effects analysis which simulates data from a proposed algorithm is presented with a missing covariate. It is an improved simulation method which involves first-order autoregressive (AR(1)) process in measuring the correlation between measurements of a subject across two time sequence. Complete-case analysis and multiple imputation method are comparatively implemented for the estimation procedure. This study shows that the multiple imputation method results in estimations which fit well to the data which are not only missing completely at random (MCAR) but also missing at random (MAR). However, the complete-case analysis results in estimators which fit well to the data which are only MCAR.