A Comparison Of Multiple Imputation Methods For Missing Data In Longitudinal Studies

Multiple imputation (MI) for imputing missing data are increasingly used in longitudinal studies where data are missing due to non-response and lost to follow-up. Standard multivariate normal imputation (MVNI) and fully conditional specifications (FCS) are the principle imputation framework available for imputing cross-sectional missing data. A number of methods has been suggested in the literature to impute longitudinal data including (i) use of standard FCS and MVNI with repeated measurements as separate distinct variables (ii) use of imputation methods based on generalized linear mixed models. No clear evaluation of the relative performance of available MI methods in the context of longitudinal data. We present a comprehensive comparison of the all the available methods for imputation longitudinal data in the context of estimating coefficient for both linear regression model and linear mixed effect model. We also compared the performance of the methods to impute both binary and continuous data. A total of 10 different methods (MVNI, JM-pan, JM-jomo, standard FCS, FCS-twofold, FCS-MTW, FCS-2lnorm, FCS-2lglm, FCS-2ljomo and FCS-Blimp) are compared in terms of bias, standard error and coverage probability of the estimated regression coefficients. These methods are compared using a simulation study based on a previously conducted analysis exploring the association between the burden of overweight and quality of life (QoL) using data from the Longitudinal Study of Australian Children (LSAC). We found that both standard FCS and MVNI provide reliable estimates and coverage of the regression parameters. Among other methods linear mixed models based methods, JM-jomo and FCS-Blimp approaches hold great promise.