Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods.
Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods.
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DOI：
10.1186/1471-2288-12-46
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发表时间：
2012-04-10
影响因子：
4
通讯作者：
White IR
中科院分区：
文献类型：
--
作者：
Seaman SR;Bartlett JW;White IR
多重插补通常用于缺失数据。当一个模型包含一个变量的多个函数作为协变量时，如何最好地插补这些协变量中的缺失值并不明显。考虑一个结果为Y、协变量为X和X2的回归。在“被动插补”中，将值X* 插补为X，然后将X2插补为（X*）2。最近的一个建议是将X2视为“只是另一个变量”（JAV），并在多变量正态性下插补X和X2。我们使用模拟来研究可以在标准软件中轻松实现的三种方法的性能：1）X对Y的线性回归以插补X，然后被动插补X2; 2）相同的回归，但具有预测均值匹配（PMM）; 3）JAV。我们还调查了类似的方法时，分析涉及的相互作用的性能，并研究JAV的理论特性。使用EPIC研究的数据说明了当存在完全或不完全混杂因素时方法的应用。当分析是具有二次项或交互作用项的线性回归并且X完全随机缺失时，JAV给出一致的估计。当X随机缺失时，JAV可能有偏倚，但这种偏倚通常小于被动插补和PMM。JAV的覆盖率通常是好的，当偏见是小的。然而，在一些具有更明显的二次效应的情况下，偏差很大，覆盖率很差。当进行逻辑回归分析时，JAV的表现有时很差。在偏倚和覆盖范围方面，PMM总体上改善了被动插补，但并未消除偏倚。考虑到现有软件的现状，JAV是一组不完美插补方法中最好的一种，适用于具有二次或交互作用效应的线性回归，但不应用于逻辑回归。
Multiple imputation is often used for missing data. When a model contains as covariates more than one function of a variable, it is not obvious how best to impute missing values in these covariates. Consider a regression with outcome Y and covariates X and X2. In 'passive imputation' a value X* is imputed for X and then X2 is imputed as (X*)2. A recent proposal is to treat X2 as 'just another variable' (JAV) and impute X and X2 under multivariate normality. We use simulation to investigate the performance of three methods that can easily be implemented in standard software: 1) linear regression of X on Y to impute X then passive imputation of X2; 2) the same regression but with predictive mean matching (PMM); and 3) JAV. We also investigate the performance of analogous methods when the analysis involves an interaction, and study the theoretical properties of JAV. The application of the methods when complete or incomplete confounders are also present is illustrated using data from the EPIC Study. JAV gives consistent estimation when the analysis is linear regression with a quadratic or interaction term and X is missing completely at random. When X is missing at random, JAV may be biased, but this bias is generally less than for passive imputation and PMM. Coverage for JAV was usually good when bias was small. However, in some scenarios with a more pronounced quadratic effect, bias was large and coverage poor. When the analysis was logistic regression, JAV's performance was sometimes very poor. PMM generally improved on passive imputation, in terms of bias and coverage, but did not eliminate the bias. Given the current state of available software, JAV is the best of a set of imperfect imputation methods for linear regression with a quadratic or interaction effect, but should not be used for logistic regression.
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影响因子：
1.8
作者：
Schenker, N;Taylor, JMG
通讯作者：
Taylor, JMG
DOI：
10.1024/0300-9831.70.5.226
发表时间：
2000-09-01
影响因子：
2.3
作者：
Brubacher, D;Moser, U;Jordan, P
通讯作者：
Jordan, P
影响因子：
2.7
作者：
Robins, JM;Wang, NS
通讯作者：
Wang, NS
影响因子：
2.7
作者：
Fine, JP
通讯作者：
Fine, JP
影响因子：
3.2
作者：
Bingham, SA;Welch, AA;Day, NE
通讯作者：
Day, NE