On Using Summary Statistics From an External Calibration Sample to Correct for Covariate Measurement Error
On Using Summary Statistics From an External Calibration Sample to Correct for Covariate Measurement Error
复制标题
DOI：
10.1097/ede.0b013e31823a4386
复制
发表时间：
2012-01-01
期刊：
影响因子：
5.4
通讯作者：
McConnell, Daniel S.
中科院分区：
文献类型：
--
作者：
Guo, Ying;Little, Roderick J.;McConnell, Daniel S.
背景：协变量测量误差在流行病学研究中很常见。目前使用来自外部校准样本的信息来纠正测量误差的方法不足以提供有效的调整后推断。我们考虑估计结果Y对协变量X和Z的回归的问题，其中Y和Z是观察到的，X是未观察到的，但观察到一个变量W测量X有误差。有关测量误差的信息在外部校准样品中提供，其中记录X和W（但不记录Y和Z）的数据。方法：我们描述了一种方法，利用校准样本的汇总统计量对回归样本中的缺失值X进行多次imputation，从而可以使用简单的多次imputation组合规则估计Y在X和Z上的回归系数以及相关的标准误差，从而在多元正态分布假设下产生有效的统计推断。结果：仿真结果表明，所提出的方法比现有方法（即朴素方法、经典校准和回归校准）提供更好的推断，特别是在校正偏差和达到名义置信水平方面。我们还用一个例子来说明我们的方法，使用线性回归来检验密歇根骨骼健康和代谢研究中中年妇女血清生殖激素浓度与骨密度损失之间的关系。结论：现有方法无法适当调整回归设置中由于测量误差引起的偏倚，特别是当测量误差较大时。所提出的方法纠正了这一缺陷。
Background: Covariate measurement error is common in epidemiologic studies. Current methods for correcting measurement error with information from external calibration samples are insufficient to provide valid adjusted inferences. We consider the problem of estimating the regression of an outcome Y on covariates X and Z, where Y and Z are observed, X is unobserved, but a variable W that measures X with error is observed. Information about measurement error is provided in an external calibration sample where data on X and W (but not Y and Z) are recorded.Methods: We describe a method that uses summary statistics from the calibration sample to create multiple imputations of the missing values of X in the regression sample, so that the regression coefficients of Y on X and Z and associated standard errors can be estimated using simple multiple imputation combining rules, yielding valid statistical inferences under the assumption of a multivariate normal distribution.Results: The proposed method is shown by simulation to provide better inferences than existing methods, namely the naive method, classical calibration, and regression calibration, particularly for correction for bias and achieving nominal confidence levels. We also illustrate our method with an example using linear regression to examine the relation between serum reproductive hormone concentrations and bone mineral density loss in midlife women in the Michigan Bone Health and Metabolism Study.Conclusions: Existing methods fail to adjust appropriately for bias due to measurement error in the regression setting, particularly when measurement error is substantial. The proposed method corrects this deficiency.