Missing binary outcomes under covariate-dependent missingness in cluster randomised trials

Anower Hossain, Karla DiazOrdaz, Jonathan W Bartlett

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9 Citations (SciVal)


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 and generalised estimating equations when binary outcomes are missing under a baseline covariate-dependent missingness mechanism. Missing outcomes were handled using complete records analysis and multilevel multiple imputation. We analytically show that cluster-level analyses for estimating risk ratio 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. On the basis of the simulation study and analytical results, we give guidance on the conditions under which each approach is valid. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Original languageEnglish
Pages (from-to)3092-3109
Number of pages18
JournalStatistics in Medicine
Issue number19
Publication statusE-pub ahead of print - 29 May 2017


  • Bias
  • Biometry
  • Cluster Analysis
  • Computer Simulation
  • Epidemiologic Methods
  • Humans
  • Logistic Models
  • Randomized Controlled Trials as Topic
  • Reproducibility of Results
  • Journal Article


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