Correcting for measurement error in fractional polynomial models using Bayesian modelling and regression calibration, with an application to alcohol and mortality.
Correcting for measurement error in fractional polynomial models using Bayesian modelling and regression calibration, with an application to alcohol and mortality.
复制标题
使用贝叶斯建模和回归校准校正分数多项式模型中的测量误差，并应用于酒精和死亡率。
DOI：
10.1002/bimj.201700279
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Keogh,RuthH
中科院分区：
文献类型：
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作者：
Gray,ChristenM;Carroll,RaymondJ;Lentjes,MarleenAH;Keogh,RuthH
暴露测量误差可能导致对暴露与结果之间关联的偏倚估计。当确定性-结果关系在适当的尺度上是线性的（例如线性、逻辑）并且测量误差是经典的（即随机噪声的结果）时，结果是效应的衰减。当关系是非线性的时，测量误差会扭曲关联的真实形状。回归校准是一种常用的测量误差校正方法，其中每个个体在结果回归模型中的未知真实暴露量被替换为其在易出错测量和任何完全测量的协变量上的期望条件。当暴露量在结果回归模型的线性预测因子中未转换时，回归校准易于执行，但当使用暴露量的非线性转换时，则不那么简单。我们描述了一种在模型中应用回归校准的方法，其中通过使用分数多项式模型转换暴露量来建模非线性关联。结果表明，采取贝叶斯估计方法是有利的。通过使用马尔可夫链蒙特卡罗算法，可以从每个人的真实暴露分布中进行采样。然后可以直接执行采样值的转换，并用于找到回归校准所需的转换后暴露的期望值。仿真研究表明，所提出的方法性能良好。我们应用该方法来研究日常饮酒与随后全因死亡率之间的关系，并使用一个误差模型来调整饮酒的间歇性性质。
Exposure measurement error can result in a biased estimate of the association between an exposure and outcome. When the exposure–outcome relationship is linear on the appropriate scale (e.g. linear, logistic) and the measurement error is classical, that is the result of random noise, the result is attenuation of the effect. When the relationship is non‐linear, measurement error distorts the true shape of the association. Regression calibration is a commonly used method for correcting for measurement error, in which each individual's unknown true exposure in the outcome regression model is replaced by its expectation conditional on the error‐prone measure and any fully measured covariates. Regression calibration is simple to execute when the exposure is untransformed in the linear predictor of the outcome regression model, but less straightforward when non‐linear transformations of the exposure are used. We describe a method for applying regression calibration in models in which a non‐linear association is modelled by transforming the exposure using a fractional polynomial model. It is shown that taking a Bayesian estimation approach is advantageous. By use of Markov chain Monte Carlo algorithms, one can sample from the distribution of the true exposure for each individual. Transformations of the sampled values can then be performed directly and used to find the expectation of the transformed exposure required for regression calibration. A simulation study shows that the proposed approach performs well. We apply the method to investigate the relationship between usual alcohol intake and subsequent all‐cause mortality using an error model that adjusts for the episodic nature of alcohol consumption.