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Stat Methods Med Res. 2016 Dec;25(6):2650-2669. Epub 2014 Apr 7.

A comparison of imputation strategies in cluster randomized trials with missing binary outcomes.

Caille A1,2,3,4, Leyrat C5,2,3, Giraudeau B5,2,3,4.

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INSERM, U1153, Paris, France
INSERM, CIC 1415, Tours, France.
CHRU de Tours, Tours, France.
Université François-Rabelais de Tours, PRES Centre-Val de Loire Université, Tours, France.
INSERM, U1153, Paris, France.


In cluster randomized trials, clusters of subjects are randomized rather than subjects themselves, and missing outcomes are a concern as in individual randomized trials. We assessed strategies for handling missing data when analysing cluster randomized trials with a binary outcome; strategies included complete case, adjusted complete case, and simple and multiple imputation approaches. We performed a simulation study to assess bias and coverage rate of the population-averaged intervention-effect estimate. Both multiple imputation with a random-effects logistic regression model or classical logistic regression provided unbiased estimates of the intervention effect. Both strategies also showed good coverage properties, even slightly better for multiple imputation with a random-effects logistic regression approach. Finally, this latter approach led to a slightly negatively biased intracluster correlation coefficient estimate but less than that with a classical logistic regression model strategy. We applied these strategies to a real trial randomizing households and comparing ivermectin and malathion to treat head lice.


cluster randomized trial; missing data; multiple imputation; outcome

[Indexed for MEDLINE]

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