Multiple imputation methods for bivariate outcomes in cluster randomised trials.
Multiple imputation methods for bivariate outcomes in cluster randomised trials.
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DOI：
10.1002/sim.6935
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发表时间：
2016-09-10
影响因子：
2
通讯作者：
Grieve R
中科院分区：
文献类型：
--
作者：
DiazOrdaz K;Kenward MG;Gomes M;Grieve R
在整群随机试验中，缺失观察值很常见。当对双变量结果进行联合建模时，这个问题更加严重，因为完整案例的比例通常远远小于完全观察到任何一个结果的比例。处理此类缺失数据的方法包括：完整病例分析、忽略聚类的单水平多重插补、每个聚类具有固定效应的多重插补和多水平多重插补。我们在一项成本效益分析中对比了处理缺失数据的替代方法，该分析使用了来自一项群集随机试验的数据，以评估护理院居民的运动干预。然后，我们进行了一项模拟研究，以评估这些方法在双变量连续结局上的性能，包括估计治疗效果的置信区间覆盖率和经验偏倚。采用全因子设计模拟随机缺失聚类数据场景。在考虑的所有缺失数据机制中，多重插补方法提供的估计值偏倚可忽略不计，而在随机化治疗组与缺失相关的情况下，完整病例分析导致治疗效应估计值偏倚。在固定效应多重插补后，置信区间覆盖率通常超过标称水平（高达99.8%），而在单水平多重插补后，置信区间覆盖率过低。多水平多重插补导致整个覆盖水平约为95%。版权所有2016作者.出版社：John Wiley & Sons Ltd
Missing observations are common in cluster randomised trials. The problem is exacerbated when modelling bivariate outcomes jointly, as the proportion of complete cases is often considerably smaller than the proportion having either of the outcomes fully observed. Approaches taken to handling such missing data include the following: complete case analysis, single‐level multiple imputation that ignores the clustering, multiple imputation with a fixed effect for each cluster and multilevel multiple imputation. We contrasted the alternative approaches to handling missing data in a cost‐effectiveness analysis that uses data from a cluster randomised trial to evaluate an exercise intervention for care home residents. We then conducted a simulation study to assess the performance of these approaches on bivariate continuous outcomes, in terms of confidence interval coverage and empirical bias in the estimated treatment effects. Missing‐at‐random clustered data scenarios were simulated following a full‐factorial design. Across all the missing data mechanisms considered, the multiple imputation methods provided estimators with negligible bias, while complete case analysis resulted in biased treatment effect estimates in scenarios where the randomised treatment arm was associated with missingness. Confidence interval coverage was generally in excess of nominal levels (up to 99.8%) following fixed‐effects multiple imputation and too low following single‐level multiple imputation. Multilevel multiple imputation led to coverage levels of approximately 95% throughout. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.