  Missing outcomes are a commonly occurring problem for cluster randomised
trials, which can lead to biased and inefficient inference if ignored or
handled inappropriately. Two approaches for analysing such trials are
cluster-level analysis and individual-level analysis. In this study, we
assessed the performance of unadjusted cluster-level analysis, baseline
covariate adjusted cluster-level analysis, random effects logistic regression
(RELR) and generalised estimating equations (GEE) when binary outcomes are
missing under a baseline covariate dependent missingness mechanism. Missing
outcomes were handled using complete records analysis (CRA) and multilevel
multiple imputation (MMI). We analytically show that cluster-level analyses for
estimating risk ratio (RR) using complete records are valid if the true data
generating model has log link and the intervention groups have the same
missingness mechanism and the same covariate effect in the outcome model. We
performed a simulation study considering four different scenarios, depending on
whether the missingness mechanisms are the same or different between the
intervention groups and whether there is an interaction between intervention
group and baseline covariate in the outcome model. Based on the simulation
study and analytical results, we give guidance on the conditions under which
each approach is valid.
