Reducing Bias Due to Exposure Measurement Error Using Disease Risk Scores.
Reducing Bias Due to Exposure Measurement Error Using Disease Risk Scores.
复制标题
使用疾病风险评分减少由于暴露测量误差造成的偏差。
DOI：
10.1093/aje/kwaa208
复制
发表时间：
2021
影响因子：
5
通讯作者：
Edwards,JessieK
中科院分区：
文献类型：
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作者：
Richardson,DavidB;Keil,AlexanderP;Cole,StephenR;Edwards,JessieK
假设一个研究者想要估计一个连续暴露变量和一个结果之间的关联，并对一组混杂因素进行调整。如果暴露变量遭受经典测量误差，其中测量的暴露在真实暴露周围分布有独立误差，则协变量调整的暴露-结果关联的估计可能有偏差。我们提出了一种方法来估计一个边际的风险结果的关联，在经典的暴露测量误差的设置使用疾病评分为基础的方法来标准化暴露的样本。首先，我们表明，建议的边际估计的风险结果的关联将遭受更少的偏见，由于经典的测量误差比协变量的条件估计的关联时，协变量是预测因素的曝光。第二，我们表明，如果暴露验证研究是可用的，以评估暴露测量误差，那么建议的边际估计的风险结果的关联可以更有效地校正测量误差比协变量条件估计的关联。我们说明这两个点使用模拟和经验的例子，使用数据的奥林达纵向研究近视（加州，1989-2001年）。
Suppose that an investigator wants to estimate an association between a continuous exposure variable and an outcome, adjusting for a set of confounders. If the exposure variable suffers classical measurement error, in which the measured exposures are distributed with independent error around the true exposure, then an estimate of the covariate-adjusted exposure-outcome association may be biased. We propose an approach to estimate a marginal exposure-outcome association in the setting of classical exposure measurement error using a disease score–based approach to standardization to the exposed sample. First, we show that the proposed marginal estimate of the exposure-outcome association will suffer less bias due to classical measurement error than the covariate-conditional estimate of association when the covariates are predictors of exposure. Second, we show that if an exposure validation study is available with which to assess exposure measurement error, then the proposed marginal estimate of the exposure-outcome association can be corrected for measurement error more efficiently than the covariate-conditional estimate of association. We illustrate both of these points using simulations and an empirical example using data from the Orinda Longitudinal Study of Myopia (California, 1989–2001).