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


Caille, A; Leyrat, C; Giraudeau, B; (2014) A comparison of imputation strategies in cluster randomized trials with missing binary outcomes. Statistical methods in medical research. ISSN 0962-2802 DOI: https://doi.org/10.1177/0962280214530030

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Abstract

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.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
PubMed ID: 24713160
Web of Science ID: 388625700016
URI: http://researchonline.lshtm.ac.uk/id/eprint/2534184

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