论文标题
实施回归校准分析的问题
Issues in Implementing Regression Calibration Analyses
论文作者
论文摘要
回归校准是一种流行的方法，用于纠正估计回归参数中的偏见，当曝光变量以误差为单位时。这种方法涉及构建校准方程，以估计鉴于容易产生错误的测量和其他混淆协变量的未知真实暴露的价值。然后，将估计的或校准的暴露替换为健康结果回归模型中的真实暴露。正确使用时，回归校准可以大大减少暴露测量误差引起的偏差。在这里，我们首先提供了回归校准的统计框架的概述，特别讨论了在估计的暴露中如何出现一种特殊类型的错误（称为伯克森错误）。然后，我们提出应用回归校准时要考虑的实际问题，包括：（1）如何开发校准方程以及包括哪些协变量； （2）计算估计回归系数的标准误差（SE）的有效方法； （3）如果校准模型中的协变量之一是暴露与结果之间关系的中介，就会出现问题。在整篇文章中，我们使用来自西班牙裔社区健康研究/拉丁裔（HCHS/SOL）和仿真的数据提供了说明性示例。我们最终提出了如何执行回归校准的建议。
Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other confounding covariates. The estimated, or calibrated, exposure is then substituted for the true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: (1) how to develop the calibration equation and which covariates to include; (2) valid ways to calculate standard errors (SE) of estimated regression coefficients; and (3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout the paper, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and simulations. We conclude with recommendations for how to perform regression calibration.
