Measurement error in a covariate or the outcome of regression models is common, but is often ignored, even though measurement error can lead to substantial bias in the estimated covariate-outcome association. While several texts on measurement error correction methods are available, these methods remain seldomly applied. To improve the use of measurement error correction methodology, we developed mecor, an R package that implements measurement error correction methods for regression models with a continuous outcome. Measurement error correction requires information about the measurement error model and its parameters. This information can be obtained from four types of studies, used to estimate the parameters of the measurement error model: an internal validation study, a replicates study, a calibration study and an external validation study. In the package mecor, regression calibration methods and a maximum likelihood method are implemented to correct for measurement error in a continuous covariate in regression analyses. Additionally, methods of moments methods are implemented to correct for measurement error in the continuous outcome in regression analyses. Variance estimation of the corrected estimators is provided in closed form and using the bootstrap.

协变量或回归模型的结果中的测量误差很常见，但经常被忽略，即使测量误差会导致估计的协变量-结果关联中的重大偏差。虽然有一些关于测量误差校正方法的文本可用，但这些方法仍然很少应用。为了改进测量误差校正方法的使用，我们开发了mecor，一个R为具有连续结果的回归模型实现测量误差校正方法的包。测量误差校正需要有关测量误差模型及其参数的信息。该信息可以从四种类型的研究中获得，用于估计测量误差模型的参数：内部验证研究、重复研究、校准研究和外部验证研究。在包mecor，回归校准方法和最大似然方法用于校正回归分析中连续协变量中的测量误差。此外，采用矩量法来校正回归分析中连续结果中的测量误差。修正估计量的方差估计以封闭形式并使用引导程序提供。