Methods: Multiple imputation model summary and model diagnostics.
REGRESSION, SENSITIVITY
Table A. Missing data summary (NAPLAN). Fig A. NAPLAN convergence. Fig B. NAPLAN density plots of observed and imputed data. Fig C. NAPLAN distribution of outcome and covariates after imputation in m = 1 dataset.(DOCX)Click here for additional data file.Figs A–D: Distribution and overlap of manually calculated stabilised weights. Fig A. AEDC imputation #1. Fig B. AEDC imputation #13. Fig C. NAPLAN imputation #1. Fig D. NAPLAN imputation #13.(DOCX)Click here for additional data file.Figs A–C: Variable standardised mean differences. Fig A. NAPLAN imputation #1 variable standardised mean differences. Fig B. NAPLAN imputation #13 variable standardised mean differences. Fig C. AEDC imputation #7 variable standardised mean differences.(DOCX)Click here for additional data file.Tables A and B. Table A. Sensitivity analysis–AEDC (special needs multiply imputed). Table B. Sensitivity analysis–AEDC (special needs excluded).(DOCX)Click here for additional data file.Tables A and B. Table A. Sensitivity analysis–NAPLAN (exempt multiply imputed). Table B. Sensitivity analysis–NAPLAN (exempt excluded).(DOCX)Click here for additional data file.Table A–Sensitivity analysis–binary outcomes TMLE.(DOCX)Click here for additional data file.Figs A and B. Fig A. Sensitivity analysis AEDC primary outcome–E-value estimation. Fig B. Sensitivity analysis NAPLAN primary outcome–E-value estimation.(DOCX)Click here for additional data file.Tables A and B. Table A. Traditional regression and treatment effect models (AEDC). Table B. Traditional regression and treatment effect models (NAPLAN).(DOCX)Click here for additional data file.