title: Missing continuous outcomes under covariate dependent missingness in cluster randomised trials.
abstract: Attrition is a common occurrence in cluster randomised trials 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 analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between 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 there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group.
authors.affiliation: 1 Department of Medical Statistics, London School of Hygiene & Tropical Medicine (LSHTM), London, UK.
authors.forename: Anower
authors.initials: A
authors.lastname: Hossain
authors.affiliation: 1 Department of Medical Statistics, London School of Hygiene & Tropical Medicine (LSHTM), London, UK.
authors.forename: Karla
authors.initials: K
authors.lastname: Diaz-Ordaz
authors.affiliation: 2 Statistical Innovation Group, AstraZeneca, Cambridge, UK.
authors.forename: Jonathan W
authors.initials: JW
authors.lastname: Bartlett
mesh_terms: D015982:Bias; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D011336:Probability; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D015203:Reproducibility of Results
keywords: Cluster randomised trials; complete records analysis; covariate dependent missingness; missing outcome data; multiple imputation