Kim M Y, Zeleniuch-Jacquotte A
Institute of Environmental Medicine and Kaplan Comprehensive Cancer Center, NYU Medical Center, New York, NY, USA.
Am J Epidemiol. 1997 Jun 1;145(11):1003-10. doi: 10.1093/oxfordjournals.aje.a009056.
The authors present a technique for correcting for exposure measurement error in the analysis of case-control data when subjects have a variable number of repeated measurements, and the average is used as the subject's measure of exposure. The true exposure as well as the measurement error are assumed to be normally distributed. The method transforms each subject's observed average by a factor which is a function of the measurement error parameters, prior to fitting the logistic regression model. The resulting logistic regression coefficient estimate based on the transformed average is corrected for error. A bootstrap method for obtaining confidence intervals for the true regression coefficient, which takes into account the variability due to estimation of the measurement error parameters, is also described. The method is applied to data from a nested case-control study of hormones and breast cancer.
作者提出了一种在病例对照数据分析中校正暴露测量误差的技术，适用于受试者有不同数量重复测量且以平均值作为受试者暴露量度的情况。假定真实暴露量和测量误差均呈正态分布。在拟合逻辑回归模型之前，该方法通过一个作为测量误差参数函数的因子对每个受试者观察到的平均值进行变换。基于变换后平均值得到的逻辑回归系数估计值会进行误差校正。还描述了一种用于获得真实回归系数置信区间的自助法，该方法考虑了测量误差参数估计所导致的变异性。该方法应用于一项关于激素与乳腺癌的巢式病例对照研究的数据。
