论文标题

易经错误的故障时间结局和暴露的近似准可能性方法

An Approximate Quasi-Likelihood Approach for Error-Prone Failure Time Outcomes and Exposures

论文作者

Boe, Lillian A., Tinker, Lesley F., Shaw, Pamela A.

论文摘要

测量误差通常在依靠电子健康记录或大型观察队列的数据的临床研究环境中产生。特别是,在慢性疾病(例如糖尿病)的队列研究中,自我报告的结果是典型的,以避免昂贵的诊断测试负担。饮食摄入量通常也是通过自我报告收集的,并受到测量误差,是与糖尿病和其他慢性疾病有关的主要因素。这些错误可能会偏向暴露 - 疾病的关联,最终可能会误导临床决策。我们已经扩展了一种基于半参数可能的方法,用于处理容易出错的,离散的故障时间结果,以解决协变量错误。我们进行了一项广泛的数值研究,将提出的方法与幼稚方法进行比较,该方法在估计感兴趣的回归参数的估计中忽略了测量误差。在考虑的所有设置中,提出的方法显示出最小的偏见和保持覆盖率的概率,因此优于幼稚分析,该分析显示出极端的偏见和低覆盖范围。该方法应用于妇女健康计划的数据,以评估能量与蛋白质摄入量与糖尿病的风险之间的关联。我们的结果表明,在自我报告的结果和饮食暴露中纠正错误的危害比估计值与忽略测量误差的分析相比,危害比估计值大不相同,这表明校正结果和协变量误差的重要性。补充材料的S1节介绍了用于实施该方法的计算细节和R代码。

Measurement error arises commonly in clinical research settings that rely on data from electronic health records or large observational cohorts. In particular, self-reported outcomes are typical in cohort studies for chronic diseases such as diabetes in order to avoid the burden of expensive diagnostic tests. Dietary intake, which is also commonly collected by self-report and subject to measurement error, is a major factor linked to diabetes and other chronic diseases. These errors can bias exposure-disease associations that ultimately can mislead clinical decision-making. We have extended an existing semiparametric likelihood-based method for handling error-prone, discrete failure time outcomes to also address covariate error. We conduct an extensive numerical study to compare the proposed method to the naive approach that ignores measurement error in terms of bias and efficiency in the estimation of the regression parameter of interest. In all settings considered, the proposed method showed minimal bias and maintained coverage probability, thus outperforming the naive analysis which showed extreme bias and low coverage. This method is applied to data from the Women's Health Initiative to assess the association between energy and protein intake and the risk of incident diabetes mellitus. Our results show that correcting for errors in both the self-reported outcome and dietary exposures leads to considerably different hazard ratio estimates than those from analyses that ignore measurement error, which demonstrates the importance of correcting for both outcome and covariate error. Computational details and R code for implementing the proposed method are presented in Section S1 of the Supplementary Materials.

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