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

被引:10
|
作者
Caille, Agnes [1 ,2 ,3 ,4 ]
Leyrat, Clemence [1 ,2 ,3 ]
Giraudeau, Bruno [1 ,2 ,3 ,4 ]
机构
[1] INSERM, U1153, Paris, France
[2] INSERM, CIC 1415, 2 Bd Tonnelle, F-37044 Tours 9, France
[3] CHRU Tours, Tours, France
[4] Univ Francois Rabelais Tours, PRES Ctr, Val de Loire Univ, Tours, France
关键词
cluster randomized trial; missing data; outcome; multiple imputation; MULTIPLE-IMPUTATION; CLINICAL-TRIALS; CORRELATION-COEFFICIENT; INTRACLASS CORRELATION; TREAT; INTENTION; DESIGN; MODELS;
D O I
10.1177/0962280214530030
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页码:2650 / 2669
页数:20
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