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Efficient semiparametric inference for two‐phase studies with outcome and covariate measurement errors
Statistics in Medicine ( IF 2 ) Pub Date : 2020-11-03 , DOI: 10.1002/sim.8799
Ran Tao 1, 2 , Sarah C Lotspeich 1 , Gustavo Amorim 1 , Pamela A Shaw 3 , Bryan E Shepherd 1
Affiliation  

In modern observational studies using electronic health records or other routinely collected data, both the outcome and covariates of interest can be error‐prone and their errors often correlated. A cost‐effective solution is the two‐phase design, under which the error‐prone outcome and covariates are observed for all subjects during the first phase and that information is used to select a validation subsample for accurate measurements of these variables in the second phase. Previous research on two‐phase measurement error problems largely focused on scenarios where there are errors in covariates only or the validation sample is a simple random sample of study subjects. Herein, we propose a semiparametric approach to general two‐phase measurement error problems with a quantitative outcome, allowing for correlated errors in the outcome and covariates and arbitrary second‐phase selection. We devise a computationally efficient and numerically stable expectation‐maximization algorithm to maximize the nonparametric likelihood function. The resulting estimators possess desired statistical properties. We demonstrate the superiority of the proposed methods over existing approaches through extensive simulation studies, and we illustrate their use in an observational HIV study.

中文翻译:

具有结果和协变量测量误差的两阶段研究的有效半参数推理

在使用电子健康记录或其他常规收集数据的现代观察性研究中,感兴趣的结果和协变量都可能容易出错,并且它们的错误通常是相关的。一种经济有效的解决方案是双阶段设计,在第一阶段观察所有受试者的易错结果和协变量,并使用该信息选择验证子样本,以便在第二阶段准确测量这些变量。先前关于两阶段测量误差问题的研究主要集中在仅协变量存在误差或验证样本是研究对象的简单随机样本的情况。在这里,我们提出了一种半参数方法来解决具有定量结果的一般双相测量误差问题,允许结果和协变量中的相关误差以及任意第二相选择。我们设计了一种计算高效且数值稳定的期望最大化算法来最大化非参数似然函数。所得的估计量具有所需的统计特性。我们通过广泛的模拟研究证明了所提出的方法相对于现有方法的优越性,并说明了它们在观察性艾滋病毒研究中的使用。
更新日期:2021-01-06
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