Techniques for handling missing data in secondary analyses of large surveys.
Techniques for handling missing data in secondary analyses of large surveys.
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DOI：
10.1016/j.acap.2010.01.005
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发表时间：
2010-05
影响因子：
3.1
通讯作者：
Lemeshow, Stanley
中科院分区：
文献类型：
--
作者：
Langkamp, Diane L.;Lehman, Amy;Lemeshow, Stanley
在对调查数据进行二次分析时，使用适当的方法处理数据缺失的情况对于减少偏见和为目标人群得出有效结论非常重要。许多已发表的使用儿童健康数据集的二次分析没有讨论用于处理缺失数据的技术，或者只是删除缺失数据的病例。缺失的数据可能会通过减少样本量而威胁到统计能力，或者在更极端的情况下，通过删除缺失值的病例得出的估计可能会有偏差，特别是如果缺失值的病例与具有完整数据的病例存在系统差异。本研究的目的是确定当不同比例的案例是缺失数据时，处理缺失数据的4种技术中哪一种最接近真实模型系数。我们进行了模拟研究，比较了所有病例都有完整数据时的模型系数，以及在10%、20%、30%或40%的病例中使用4种处理缺失数据的技术时的模型系数。当超过10%的病例有缺失数据时，重新加权和多次代入技术优于删除缺失分数的病例或热甲板代入。这些发现表明，儿童健康研究人员在分析调查数据时应该谨慎，如果有很大比例的病例存在缺失值。在大多数情况下，应该不鼓励放弃丢失数据的案例的技术。如果有很大比例的病例缺少数据，调查人员应考虑重新加权或多重归算。
Using an appropriate method to handle cases with missing data when performing secondary analyses of survey data is important to reduce bias and to reach valid conclusions for the target population. Many published secondary analyses using child health data sets do not discuss the technique employed to treat missing data or simply delete cases with missing data. Missing data may threaten statistical power by reducing sample size or, in more extreme situations, estimates derived by deleting cases with missing values may be biased, particularly if the cases with missing values are systematically different from those with complete data. The aim of this study was to determine which of 4 techniques for handling missing data most closely estimates the true model coefficient when varying proportions of cases are missing data. We performed a simulation study to compare model coefficients when all cases had complete data and when 4 techniques for handling missing data were employed with 10%, 20%, 30% or 40% of the cases missing data. When more than 10% of the cases had missing data, the re-weight and multiple imputation techniques were superior to dropping cases with missing scores or hot deck imputation. These findings suggest that child health researchers should use caution when analyzing survey data if a large percentage of cases have missing values. In most situations, the technique of dropping cases with missing data should be discouraged. Investigators should consider re-weighting or multiple imputation, if a large percentage of cases are missing data.
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