Additional file 1 of Evaluation of approaches for multiple imputation of three-level data
Creators: name: Wijesuriya, Rushani
name: Moreno-Betancur, Margarita
name: Carlin, John B.
name: Lee, Katherine J.
Publisher: name: figshare
Year: 2020
Subjects: subject: Statistics
subject: FOS: Mathematics
subject: FOS: Mathematics

description: Additional file 1: Table S1, S2 and S3. contains the parameter values used in the data generating and missing data generation models of the simulation study. Table S4, S5 and S6 contains the values of performance measures, for available case analysis (ACA) and 8 multiple imputation (MI) approaches for estimating the regression coefficient of depressive symptom scores at the previous wave, under the three missing data mechanisms missing completely at random (MCAR), missing at random similar to CATS (MAR-CATS) and inflated missing at random (MAR-inflated) respectively. Table S7 and S8. contains the values of performance measures, for ACA and 8 multiple imputation (MI) approaches in estimating the variance components at level 3, 2 and 1, when data are missing completely at random (MCAR) with low (10, 15 and 20% across waves 2, 4 and 6 respectively) and high (20, 30 and 40% across waves 2, 4 and 6 respectively missing data percentages across waves respectively. Table S9 and S10. contains the values of performance measures, for ACA and 8 multiple imputation (MI) approaches in estimating the variance components at level 3, 2 and 1, when data missing at random (MAR-CATS) with low (10, 15 and 20% across waves 2, 4 and 6 respectively) and high (20, 30 and 40% across waves 2, 4 and 6 respectively missing data percentages across waves respectively. Table S11 and S12. contains the values of performance measures, for ACA and 8 multiple imputation (MI) approaches in estimating the variance components at level 3, 2 and 1, when data are missing at random (MAR-inflated) with low (10, 15 and 20% across waves 2, 4 and 6 respectively) and high (20, 30 and 40% across waves 2, 4 and 6 respectively missing data percentages respectively. Table S13. contains the estimated regression coefficients (and standard errors) for the adjusting covariates, from available case analysis (ACA) and 8 MI approaches applied to the CATS data analysis. Fig. S1 and S2. shows the distribution of the bias in the estimated regression coefficient of interest across the 1000 replications for available case analysis (ACA) and the 8 multiple imputation (MI) approaches under two scenarios for missing data proportions at waves 2, 4 and 6 (10, 15, 20 and 20%, 30, 40%, respectively) and four ICC combinations when data are missing completely at random (MCAR) and missing at random (MAR-inflated) respectively. Fig. S3 and S4. shows the empirical standard errors (filled circles with error bars showing ±1.96× Monte Carlo standard errors) and average model-based standard errors (hollow circles) from 1000 replications, for available case analysis (ACA) and the 8 multiple imputation (MI) approaches under two scenarios for missing data proportions at waves 2,4 and 6 (10, 15, 20 and 20%, 30, 40%, respectively) and four ICC combinations when data are missing completely at random (MCAR) and missing at random (MAR-inflated) respectively. Fig. S5 and S6. shows the estimated bias in the variance components at level 1, 2 and 3 across the 1000 replications available case analysis (ACA) and the 8 multiple imputation (MI) approaches under two scenarios for missing data proportions at waves 2, 4 and 6 (10, 15, 20 and 20%, 30, 40%, respectively) and four ICC combinations when data are missing completely at random (MCAR) and missing at random (MAR-inflated) respectively.