Analysis using multiply imputed and complete case data
REGRESSION, SECONDARY
As pre-specified in the protocol (Additional file CONSORT flow chartFor multiple imputation, multiply imputed datasets were first generated using separate imputation models for each trial. Compared to those who completed the follow-up survey, participants who were lost to follow-up differed on several baseline demographic characteristics, also called auxiliary variables, included in each imputation model (Additional file We observed a monotone missing data pattern for the primary and secondary outcomes, i.e., if one of the outcomes was missing, then all other outcomes (gathered from the follow-up survey) were also missing. Multiple imputation by sequential regression was performed such that the primary outcome was imputed first given the observed data (auxiliary variables and their interaction terms with arm). Then, this imputed primary outcome was used as a predictor in the next model to impute one of the secondary outcomes; this process continued so that the final model for the last secondary outcome utilized all previously imputed outcomes as predictors.The number of multiply imputed datasets (