Review of inverse probability weighting for dealing with missing data
Review of inverse probability weighting for dealing with missing data
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DOI：
10.1177/0962280210395740
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发表时间：
2013-06-01
影响因子：
2.3
通讯作者：
White, Ian R.
中科院分区：
文献类型：
--
作者：
Seaman, Shaun R.;White, Ian R.
处理缺失数据的最简单方法是将分析限制在完整的案例中，即没有缺失值的个体。然而，这可能会导致偏见。逆概率加权（IPW）是一种常用的方法来纠正这种偏见。它还用于调整抽样调查中的不平等抽样比例。本文就IPW在流行病学研究中的应用作一综述。我们描述了如何在完整的情况下分析的偏见，以及如何IPW可以删除it.IPW相比，多重插补（MI），我们解释了为什么，尽管MI通常是更有效的，IPW有时可能是首选。我们讨论了丢失模型的选择和方法，如重量截断，重量稳定和增强IPW。IPW的使用说明了1958年英国出生队列的数据。
The simplest approach to dealing with missing data is to restrict the analysis to complete cases, i.e. individuals with no missing values. This can induce bias, however. Inverse probability weighting (IPW) is a commonly used method to correct this bias. It is also used to adjust for unequal sampling fractions in sample surveys. This article is a review of the use of IPW in epidemiological research. We describe how the bias in the complete-case analysis arises and how IPW can remove it. IPW is compared with multiple imputation (MI) and we explain why, despite MI generally being more efficient, IPW may sometimes be preferred. We discuss the choice of missingness model and methods such as weight truncation, weight stabilisation and augmented IPW. The use of IPW is illustrated on data from the 1958 British Birth Cohort.