Missing continuous outcomes under covariate dependent missingness in cluster randomised trials.
Missing continuous outcomes under covariate dependent missingness in cluster randomised trials.
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DOI：
10.1177/0962280216648357
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发表时间：
2017-06
影响因子：
2.3
通讯作者：
Bartlett JW
中科院分区：
文献类型：
--
作者：
Hossain A;Diaz-Ordaz K;Bartlett JW
磨损是集群随机试验中的常见现象，它会导致结果数据丢失。分析这类试验的两种方法是组群分析和个人分析。本文比较了基线协变量调整后的聚类水平分析、基线协变量调整后的聚类水平分析和线性混合模型分析在连续结果中基线协变量相依缺失情况下的偏差、平均估计标准误差和覆盖概率。对于遗漏的结果数据，采用完整记录分析和多重填补的方法进行处理。我们考虑了四种情况，在干预组之间，失配机制和基线协变量对结果的影响相同或不同。我们发现，只有当两个干预组都有相同的失配机制，并且基线协变量和干预组之间没有交互作用时，未调整的聚类级分析和基线协变量调整的聚类级分析都给出了干预效果的无偏估计。线性混合模型和多重归因给出了所有四种考虑情景下的无偏估计，前提是干预和基线协变量的交互作用在适当的时候被包括在模型中。在集群随机试验中，集群均值补偿已被认为是处理缺失结果的一种有效方法。我们发现，当干预组间的错配机制相同且基线协变量与干预组之间不存在交互作用时，聚类均值估计才能给出无偏估计。多重归因显示，每个干预组中有少量簇的覆盖范围过大。
Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group.