  Attrition is a common occurrence in cluster randomised trials (CRTs) which
leads to missing outcome data. Two approaches for analysing such trials are
cluster-level analysis and individual-level analysis. This paper compares the
performance of unadjusted cluster-level analysis, baseline covariate adjusted
cluster-level analysis and linear mixed model (LMM) analysis, under baseline
covariate dependent missingness (CDM) in continuous outcomes, in terms of bias,
average estimated standard error and coverage probability. The methods of
complete case analysis (CCA) and multiple imputation (MI) are used to handle
the missing outcome data. Four possible scenarios are considered depending on
whether the missingness mechanisms and covariate effects on outcome are the
same or different in the two intervention groups. We show that both unadjusted
cluster-level analysis and baseline covariate adjusted cluster-level analysis
give unbiased estimates of the intervention effect only if both intervention
groups have the same missingness mechanisms and the same covariate effects,
which is arguably unlikely to hold in practice. LMM and MI give unbiased
estimates under all four considered scenarios, provided that an interaction of
intervention indicator and covariate is included in the model when the
covariate effects are different in the two intervention groups. MI gives
slightly overestimation of average standard error, which leads to a decrease in
power.
