Wong M, Day N E
Department of Mathematics, Hong Kong University of Science and Technology, PRC.
J Epidemiol Biostat. 2000;5(6):331-7.
In epidemiological studies, estimation of disease exposure associations will be biased if the exposure is measured with error. In previous papers, we considered the validity of an estimator of the correction factor under a variety of assumptions. In particular, in both univariate and bivariate cases, we considered the error in estimating the correction factor induced by incorrect assumptions on the independent errors of repeated measures, or of different types of measures.
We concentrate our discussion in this paper on the optimal design of the validation study based on the asymptotic variance of the estimate of the correction factor. Only the univariate situation is considered. We also present an example to illustrate the importance of suitable design.
The value of a good biomarker is demonstrated again.
在流行病学研究中,如果暴露的测量存在误差,那么疾病暴露关联的估计将会产生偏差。在之前的论文中,我们在各种假设下考虑了校正因子估计量的有效性。特别是,在单变量和双变量情况下,我们考虑了由于对重复测量或不同类型测量的独立误差做出不正确假设而导致的校正因子估计误差。
本文我们基于校正因子估计量的渐近方差,将讨论集中在验证研究的最优设计上。仅考虑单变量情况。我们还给出一个例子来说明合适设计的重要性。
再次证明了良好生物标志物的价值。