AUTHOR=Li Meijuan , Chen Nan , Cui Yang , Liu Hongyun TITLE=Comparison of Different LGM-Based Methods with MAR and MNAR Dropout Data JOURNAL=Frontiers in Psychology VOLUME=8 YEAR=2017 URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2017.00722 DOI=10.3389/fpsyg.2017.00722 ISSN=1664-1078 ABSTRACT=
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