论文标题
校正分段COX模型中的测量误差
Correcting for Measurement Error in Segmented Cox Model
论文作者
论文摘要
主要兴趣协变量的测量误差（例如，暴露变量或危险因素）在流行病学和健康研究中很常见。它可以影响从拟合回归模型得出的相对风险估计器或其他类型的系数。为了执行测量误差分析，人们需要有关误差结构的信息。验证数据的两个来源是主要数据的内部子集，以及外部或独立研究。对于两个来源，测量了真实的协变量（即没有错误），或者是多次测量了容易发生的协变量的替代物（重复测量）。本文使用偏置校正方法RC和RR比较了COX模型中不同验证源的估计中的精度。介绍了每个验证源下的理论属性。在一项模拟研究中，发现从较小的平方错误和较窄的置信区间来看，最佳验证源是内部验证，在常见疾病病例中衡量了真实协变量，并且对稀有病例的替代物的重复测量进行了外部验证。此外，发现需要解决真实协变量与其替代物之间的相关性，以及需要变更点的价值，尤其是在罕见病病例中。
Measurement error in the covariate of main interest (e.g. the exposure variable, or the risk factor) is common in epidemiologic and health studies. It can effect the relative risk estimator or other types of coefficients derived from the fitted regression model. In order to perform a measurement error analysis, one needs information about the error structure. Two sources of validation data are an internal subset of the main data, and external or independent study. For the both sources, the true covariate is measured (that is, without error), or alternatively, its surrogate, which is error-prone covariate, is measured several times (repeated measures). This paper compares the precision in estimation via the different validation sources in the Cox model with a changepoint in the main covariate, using the bias correction methods RC and RR. The theoretical properties under each validation source is presented. In a simulation study it is found that the best validation source in terms of smaller mean square error and narrower confidence interval is the internal validation with measure of the true covariate in a common disease case, and the external validation with repeated measures of the surrogate for a rare disease case. In addition, it is found that addressing the correlation between the true covariate and its surrogate, and the value of the changepoint, is needed, especially in the rare disease case.
