关于处理条件缺失值的说明

Global Epidemiology Pub Date : 2024-09-21 DOI:10.1016/j.gloepi.2024.100164

Mohammad Ali Mansournia , Maryam Nazemipour , Mahyar Etminan

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引用次数: 0

摘要

在医学研究中，有些变量是根据另一个变量的某些水平有条件地定义的，这就导致了有条件的缺失数据。鉴于回归模型中固有的列表式删除效率低下，需要对这类结构性缺失数据进行估算。我们将通过一些实例，说明在病因学和预测学研究中使用简单估算程序处理条件缺失值的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A note on handling conditional missing values

In medical research, some variables are conditionally defined on some levels of another variable, leading to conditional missing data. Imputation of this type of structural missing data is needed given inefficiency of listwise deletion inherent in regression modeling. Using some examples, we illustrate handling of conditional missing values using simple imputation procedures in etiologic and prediction research.

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来源期刊

Global Epidemiology Medicine-Infectious Diseases

CiteScore

5.00

自引率

0.00%

发文量

审稿时长

39 days