A Comparison of Imputation Strategies in Cluster Randomized Trials with Missing Binary Outcomes - Archive ouverte HAL
Article Dans Une Revue Statistical Methods in Medical Research Année : 2016

A Comparison of Imputation Strategies in Cluster Randomized Trials with Missing Binary Outcomes

Résumé

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.
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Dates et versions

hal-03157717 , version 1 (31-03-2021)

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Agnès Caille, Clémence Leyrat, Bruno Giraudeau. A Comparison of Imputation Strategies in Cluster Randomized Trials with Missing Binary Outcomes. Statistical Methods in Medical Research, 2016, 25 (6), pp.2650--2669. ⟨10.1177/0962280214530030⟩. ⟨hal-03157717⟩
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