Spiegelman D, Zhao B, Kim J
Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.
Stat Med. 2005 Jun 15;24(11):1657-82. doi: 10.1002/sim.2055.
A measurement error model proposed previously allows for correlations between subject-specific biases and between random within-subject errors in the surrogates obtained from two modes of measurement. However, most of these model parameters are not identifiable from the standard validation study design, including, importantly, the attenuation factor needed to correct for bias in relative risk estimates due to measurement error. We propose validation study designs that permit estimation and inference for the attenuation factor and other parameters of interest when these correlations are present. We use an estimating equations framework to develop semi-parametric estimators for these parameters, exploiting instrumental variables techniques. The methods are illustrated through application to data from the Nurses' Health Study and Health Professionals' Follow-up Study, and comparisons are made to more restrictive models.
先前提出的一个测量误差模型考虑了特定受试者偏差之间以及从两种测量模式获得的替代指标中受试者内随机误差之间的相关性。然而，这些模型参数中的大多数无法从标准验证研究设计中识别出来，重要的是，还包括校正测量误差导致的相对风险估计偏差所需的衰减因子。我们提出了验证研究设计，当存在这些相关性时，允许对衰减因子和其他感兴趣的参数进行估计和推断。我们使用一个估计方程框架，利用工具变量技术为这些参数开发半参数估计量。通过应用于护士健康研究和卫生专业人员随访研究的数据来说明这些方法，并与限制更多的模型进行比较。
