Comparison of Different LGM-Based Methods with MAR and MNAR Dropout Data

The missing not at random (MNAR) mechanism may bias parameter estimates and even distort study results. This study compared the maximum likelihood (ML) selection model based on missing at random (MAR) mechanism and the Diggle-Kenward selection model based on MNAR mechanism for handling missing data through a Monte Carlo simulation study. Four factors were considered, including the missingness mechanism, the dropout rate, the distribution shape (i.e., skewness and kurtosis), and the sample size. The results indicated that: (1) Under the MAR mechanism, the Diggle-Kenward selection model yielded similar estimation results with the ML approach; Under the MNAR mechanism, the results of ML approach were underestimated, especially for the intercept mean and intercept slope (μ i and μ s ). (2) Under the MAR mechanism, the 95% CP of the Diggle-Kenward selection model was lower than that of the ML method; Under the MNAR mechanism, the 95% CP for the two methods were both under the desired level of 95%, but the Diggle-Kenward selection model yielded much higher coverage probabilities than the ML method. (3) The Diggle-Kenward selection model was easier to be influenced by the non-normal degree of target variable's distribution than the ML approach. The level of dropout rate was the major factor affecting the parameter estimation precision, and the dropout rate-induced difference of two methods can be ignored only when the dropout rate falls below 10%.