Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Stat Med. 2021 Jan 30;40(2):271-286. doi: 10.1002/sim.8773. Epub 2020 Oct 21.
Measurement error arises through a variety of mechanisms. A rich literature exists on the bias introduced by covariate measurement error and on methods of analysis to address this bias. By comparison, less attention has been given to errors in outcome assessment and nonclassical covariate measurement error. We consider an extension of the regression calibration method to settings with errors in a continuous outcome, where the errors may be correlated with prognostic covariates or with covariate measurement error. This method adjusts for the measurement error in the data and can be applied with either a validation subset, on which the true data are also observed (eg, a study audit), or a reliability subset, where a second observation of error prone measurements are available. For each case, we provide conditions under which the proposed method is identifiable and leads to consistent estimates of the regression parameter. When the second measurement on the reliability subset has no error or classical unbiased measurement error, the proposed method is consistent even when the primary outcome and exposures of interest are subject to both systematic and random error. We examine the performance of the method with simulations for a variety of measurement error scenarios and sizes of the reliability subset. We illustrate the method's application using data from the Women's Health Initiative Dietary Modification Trial.
测量误差是由多种机制引起的。关于协变量测量误差引起的偏差以及分析方法的研究已经有很多文献，相比之下，对于结果评估和非经典协变量测量误差的关注较少。我们考虑将回归校准方法扩展到连续结果存在误差的情况，其中误差可能与预后协变量或协变量测量误差相关。该方法可以校正数据中的测量误差，并可应用于具有真实数据的验证子集（例如，研究审核）或易出错测量的第二观测的可靠性子集。对于每种情况，我们都提供了所提出的方法可识别的条件，并导致回归参数的一致估计。当可靠性子集上的第二次测量没有误差或经典无偏测量误差时，即使主要结果和感兴趣的暴露都受到系统误差和随机误差的影响，所提出的方法也是一致的。我们使用各种测量误差情况和可靠性子集的大小进行了模拟，以检验该方法的性能。我们使用妇女健康倡议饮食干预试验的数据说明了该方法的应用。
