Adjusting for partially missing baseline measurements in randomized trials
Adjusting for partially missing baseline measurements in randomized trials
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DOI：
10.1002/sim.1981
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发表时间：
2005-04-15
影响因子：
2
通讯作者：
Thompson, SG
中科院分区：
文献类型：
--
作者：
White, IR;Thompson, SG
在随机试验中调整基线变量可以提高检测治疗效果的能力。然而，当部分基线数据缺失时，对完整病例的分析效率低下。我们考虑在基线和结果变量正态分布的情况下各种可能的改进。基线和结果的联合建模是最有效的方法。平均插补是一个很好的选择，但需要满足三个条件。首先，如果基线和结果的相关性超过 0.6，则应使用加权，以便从完整病例中获得更多信息。其次，插补应该以确定性的方式进行，如果可能的话使用其他基线变量，但不使用随机组或结果。第三，如果基线不是完全随机缺失的，则应包含缺失的虚拟变量作为协变量（缺失指标方法）。这些方法在社区精神病学的一项随机试验中得到了说明。版权所有 (c) 2004 John Wiley & Sons, Ltd.
Adjustment for baseline variables in a randomized trial can increase power to detect a treatment effect. However, when baseline data are partly missing, analysis of complete cases is inefficient. We consider various possible improvements in the case of normally distributed baseline and outcome variables. Joint modelling of baseline and outcome is the most efficient method. Mean imputation is an excellent alternative, subject to three conditions. Firstly, if baseline and outcome are correlated more than about 0.6 then weighting should be used to allow for the greater information from complete cases. Secondly, imputation should be carried out in a deterministic way, using other baseline variables if possible, but not using randomized arm or outcome. Thirdly, if baselines are not missing completely at random, then a dummy variable for missingness should be included as a covariate (the missing indicator method). The methods are illustrated in a randomized trial in community psychiatry. Copyright (c) 2004 John Wiley & Sons, Ltd.