Multiple Imputation by Chained Equations (MICE): Implementation in Stata
Multiple Imputation by Chained Equations (MICE): Implementation in Stata
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DOI：
10.18637/jss.v045.i04
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发表时间：
2011-12-01
影响因子：
5.8
通讯作者：
White, Ian R.
中科院分区：
文献类型：
--
作者：
Royston, Patrick;White, Ian R.
缺失数据在真实数据集中很常见。对于医学流行病学和预后因素研究，多重插补正在成为在随机缺失假设下估计缺失协变量数据的模型的标准途径。我们描述了ice，它是MICE 多重插补方法在Stata 中的实现。卵巢癌观察性研究的真实数据用于说明冰敷的众多选择中最重要的一个。我们简要评论 Stata 版本 11 和 12 中引入的新数据库架构和多重插补程序。
Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors studies in medicine, multiple imputation is becoming the standard route to estimating models with missing covariate data under a missing-at-random assumption. We describe ice, an implementation in Stata of the MICE approach to multiple imputation. Real data from an observational study in ovarian cancer are used to illustrate the most important of the many options available with ice. We remark briefly on the new database architecture and procedures for multiple imputation introduced in releases 11 and 12 of Stata.