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• W2951223099 abstract "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." @default.
• W2951223099 created "2019-06-27" @default.
• W2951223099 creator A5000460289 @default.
• W2951223099 creator A5010342264 @default.
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• W2951223099 date "2016-03-14" @default.
• W2951223099 modified "2023-10-18" @default.
• W2951223099 title "Missing binary outcomes under covariate dependent missingness in cluster randomised trials" @default.
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• W2951223099 hasPublicationYear "2016" @default.
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