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.
翻译:在用错误测量暴露变量时,回归度校准是一种常用的方法,用于纠正估计回归值中的偏差。这一方法涉及建立一个校准方程,以估计根据易出错的测量和其他混杂的共差值而得出的未知真实暴露值。估计或校准的暴露值随后在健康结果回归模型中被替换为真实暴露值。如果使用得当,回归度校准可大大降低暴露测量误差引发的偏差。这里,我们首先提供回归度校准统计框架概览,具体讨论在估计暴露中如何出现一种特殊类型的错误,称为伯克森误差。然后我们提出实际问题,以便在应用回归度校准时加以考虑,包括:(1) 如何开发校准方程,并包括哪些变量;(2) 计算估计回归系数的标准误差(SE)的有效方法;以及(3) 如果校准模型中的一个共差是暴露与结果关系的中间点,则会产生问题。在整个文件中,我们提供示例,我们使用西班牙裔社区健康研究/拉美人健康研究(CHINS/SOL)和模拟分析数据,我们得出如何进行回归分析的建议。