Multiple Imputation for Missing Data: Fully Conditional Specification Versus Multivariate Normal Imputation
Multiple Imputation for Missing Data: Fully Conditional Specification Versus Multivariate Normal Imputation
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DOI：
10.1093/aje/kwp425
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发表时间：
2010-03-01
影响因子：
5
通讯作者：
Carlin, John B.
中科院分区：
文献类型：
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作者：
Lee, Katherine J.;Carlin, John B.
流行病学研究中的统计分析经常受到数据缺失的阻碍，多重插补越来越多地被用来处理这个问题。在一项模拟研究中，作者比较了标准软件中广泛使用的两种插补方法：完全条件规范 (FCS) 或“链式方程”和多元正态插补 (MVNI)。作者创建了 1,000 个观察值的数据集来模拟队列研究，并在 3 种缺失数据机制下诱导缺失数据。使用 Stata（德克萨斯州大学城 Stata 公司）中的 FCS（Royston 的“ice”）和 MVNI（Schafer 的 NORM）进行插补，并使用变换或预测匹配来管理连续变量中的非正态性。对这些方法和完整案例分析之间的一组回归参数的推论进行了比较。正如预期的那样，FCS 和 MVNI 通常比完整病例分析的偏差更小，并且尽管存在明显不遵循正态分布的二元和序数变量，但两者都产生了相似的结果。尽管其他参数的推论基本上不受影响，但忽略连续协变量中的偏度会导致两种方法下相应回归参数的偏差较大且覆盖范围较差。这些结果让我们确信，在涉及不同尺度变量的标准回归分析中，FCS 和 MVNI 可以预期得到类似的结果。
Statistical analysis in epidemiologic studies is often hindered by missing data, and multiple imputation is increasingly being used to handle this problem. In a simulation study, the authors compared 2 methods for imputation that are widely available in standard software: fully conditional specification (FCS) or "chained equations" and multivariate normal imputation (MVNI). The authors created data sets of 1,000 observations to simulate a cohort study, and missing data were induced under 3 missing-data mechanisms. Imputations were performed using FCS (Royston's "ice") and MVNI (Schafer's NORM) in Stata (Stata Corporation, College Station, Texas), with transformations or prediction matching being used to manage nonnormality in the continuous variables. Inferences for a set of regression parameters were compared between these approaches and a complete-case analysis. As expected, both FCS and MVNI were generally less biased than complete-case analysis, and both produced similar results despite the presence of binary and ordinal variables that clearly did not follow a normal distribution. Ignoring skewness in a continuous covariate led to large biases and poor coverage for the corresponding regression parameter under both approaches, although inferences for other parameters were largely unaffected. These results provide reassurance that similar results can be expected from FCS and MVNI in a standard regression analysis involving variously scaled variables.