Bias Correction Methods for Misclassified Covariates in the Cox Model: comparison offive correction methods by simulation and data analysis.
Bias Correction Methods for Misclassified Covariates in the Cox Model: comparison offive correction methods by simulation and data analysis.
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DOI：
10.1080/15598608.2013.772830
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发表时间：
2013-01-01
影响因子：
0.6
通讯作者：
Rose KM
中科院分区：
文献类型：
--
作者：
Bang H;Chiu YL;Kaufman JS;Patel MD;Heiss G;Rose KM
在研究中，当变量(S)不能被准确测量时，测量误差/误分类是很常见的。在各种环境和背景下，已经开发了许多统计方法来解决这一问题。然而，在COX比例风险回归模型中，分类暴露变量(S)的处理方法相对较少。在本文中，我们旨在通过模拟来回顾和比较处理这一问题的不同方法--朴素方法、回归校正方法、合并估计方法、多重补偿方法、修正分数估计方法和MC-SIMEX方法。这些方法也被应用于生命过程的研究，记录了数据和历史记录。在实践中，应尽可能在设计和分析中考虑到测量误差/分类错误的问题。此外，在分析中，在适当理解基本假设的情况下，实施一种以上的估计和推断校正方法可能更为理想。
Measurement error/misclassification is commonplace in research when variable(s) can notbe measured accurately. A number of statistical methods have been developed to tackle this problemin a variety of settings and contexts. However, relatively few methods are available to handlemisclassified categorical exposure variable(s) in the Cox proportional hazards regression model. Inthis paper, we aim to review and compare different methods to handle this problem - naïvemethods, regression calibration, pooled estimation, multiple imputation, corrected score estimation,and MC-SIMEX - by simulation. These methods are also applied to a life course study with recalleddata and historical records. In practice, the issue of measurement error/misclassification should beaccounted for in design and analysis, whenever possible. Also, in the analysis, it could be moreideal to implement more than one correction method for estimation and inference, with properunderstanding of underlying assumptions.