Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect.
Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect.
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DOI：
10.1080/00949655.2014.907801
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发表时间：
2015
影响因子：
1.2
通讯作者：
Kong M
中科院分区：
文献类型：
--
作者：
Wan Y;Datta S;Conklin DJ;Kong M
当存在缺失的协变量时，变量选择和预测的统计方法可能具有挑战性。尽管多重插补（MI）是解决缺失数据问题的普遍接受的技术，但如何结合MI结果进行变量选择尚不清楚，因为不同的插补可能会导致不同的选择。广泛应用的变量选择方法包括稀疏偏最小二乘法（SPLS）和惩罚最小二乘法，例如弹性网（ENet）方法。在本文中，我们提出了一种基于 MI 的加权弹性网络（MI-WENet）方法，该方法基于堆叠的 MI 数据和堆叠数据集中每个观测值的加权方案。在 MI-WENet 方法中，MI 考虑了缺失值的采样和插补不确定性，权重考虑了观察到的信息。我们进行了广泛的数值模拟，以将所提出的 MI-WENet 方法与其他竞争替代方法（例如 SPLS 和 ENet）进行比较。此外，我们应用 MIWENet 方法来检查内皮功能的预测变量，这些变量可以在体外去氧肾上腺素诱导的延伸和乙酰胆碱诱导的松弛实验中通过中位有效剂量 (ED50) 和最大效应 (Emax) 来表征。
The statistical methods for variable selection and prediction could be challenging when missing covariates exist. Although multiple imputation (MI) is a universally accepted technique for solving missing data problem, how to combine the MI results for variable selection is not quite clear, because different imputations may result in different selections. The widely applied variable selection methods include the sparse partial least-squares (SPLS) method and the penalized least-squares method, e.g. the elastic net (ENet) method. In this paper, we propose an MI-based weighted elastic net (MI-WENet) method that is based on stacked MI data and a weighting scheme for each observation in the stacked data set. In the MI-WENet method, MI accounts for sampling and imputation uncertainty for missing values, and the weight accounts for the observed information. Extensive numerical simulations are carried out to compare the proposed MI-WENet method with the other competing alternatives, such as the SPLS and ENet. In addition, we applied the MIWENet method to examine the predictor variables for the endothelial function that can be characterized by median effective dose (ED50) and maximum effect (Emax) in an ex-vivo phenylephrine-induced extension and acetylcholine-induced relaxation experiment.