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首页> 外文期刊>Biometrics: Journal of the Biometric Society : An International Society Devoted to the Mathematical and Statistical Aspects of Biology >Binary regression analysis with pooled exposure measurements: a regression calibration approach.
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Binary regression analysis with pooled exposure measurements: a regression calibration approach.

机译：合并暴露量的二元回归分析：回归校准方法。

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摘要

It has become increasingly common in epidemiological studies to pool specimens across subjects to achieve accurate quantitation of biomarkers and certain environmental chemicals. In this article, we consider the problem of fitting a binary regression model when an important exposure is subject to pooling. We take a regression calibration approach and derive several methods, including plug-in methods that use a pooled measurement and other covariate information to predict the exposure level of an individual subject, and normality-based methods that make further adjustments by assuming normality of calibration errors. Within each class we propose two ways to perform the calibration (covariate augmentation and imputation). These methods are shown in simulation experiments to effectively reduce the bias associated with the naive method that simply substitutes a pooled measurement for all individual measurements in the pool. In particular, the normality-based imputation method performs reasonably well in a variety of settings, even under skewed distributions of calibration errors. The methods are illustrated using data from the Collaborative Perinatal Project.
机译：在流行病学研究中，越来越多的标本跨对象汇集以实现生物标志物和某些环境化学物质的准确定量。在本文中，我们考虑了在重要风险暴露合并时拟合二元回归模型的问题。我们采用回归校准方法，并得出了几种方法，包括使用合并的测量值和其他协变量信息来预测单个对象的暴露水平的插件方法，以及通过假设校准误差的正态性进行进一步调整的基于正态性的方法。 。在每个课程中，我们提出两种执行校准的方法（协变量增量和插补）。在仿真实验中显示了这些方法，可以有效地减少与仅将合并的度量值替换为合并中的所有单独度量值的天真的方法相关的偏差。尤其是，即使在校准误差偏斜分布的情况下，基于正态性的插补方法在各种设置下也表现良好。使用协作围产期项目的数据说明了这些方法。

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