Obtaining Predictions from Models Fit to Multiply Imputed Data
Obtaining Predictions from Models Fit to Multiply Imputed Data
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DOI：
10.1177/0049124115610345
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发表时间：
2016-02-01
影响因子：
6.3
通讯作者：
Miles, Andrew
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文献类型：
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作者：
Miles, Andrew
从拟合多重插补数据的回归模型中获得预测可能具有挑战性，因为多重插补的处理很少对如何计算预测提供明确的指导，并且因为可用的软件通常没有用于执行必要计算的内置例程。本研究报告回顾了如何使用鲁宾规则获得预测，即在每个估算数据集中分别估计，然后合并。然后，它表明预测也可以直接从最终分析模型计算。当预测仅依赖于系数的线性变换时，这两种方法产生相同的结果，并使用delta方法计算标准误差，当使用非线性变换时，这两种方法仅略有不同。然而，从最终模型的计算更快，更容易实现，并生成与模型系数有更清晰关系的预测。这些原则说明使用的数据从一般社会调查和模拟。
Obtaining predictions from regression models fit to multiply imputed data can be challenging because treatments of multiple imputation seldom give clear guidance on how predictions can be calculated, and because available software often does not have built-in routines for performing the necessary calculations. This research note reviews how predictions can be obtained using Rubin's rules, that is, by being estimated separately in each imputed data set and then combined. It then demonstrates that predictions can also be calculated directly from the final analysis model. Both approaches yield identical results when predictions rely solely on linear transformations of the coefficients and calculate standard errors using the delta method and diverge only slightly when using nonlinear transformations. However, calculation from the final model is faster, easier to implement, and generates predictions with a clearer relationship to model coefficients. These principles are illustrated using data from the General Social Survey and with a simulation.