Modelling complex mixtures in epidemiologic analysis: additive versus relative measures for differential effectiveness
Modelling complex mixtures in epidemiologic analysis: additive versus relative measures for differential effectiveness
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DOI：
10.1136/oemed-2013-101665
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发表时间：
2014-02-01
影响因子：
4.9
通讯作者：
Daniels, Robert D.
中科院分区：
文献类型：
--
作者：
Hamra, Ghassan Badri;MacLehose, Richard;Daniels, Robert D.
目标混合暴露通常使用加权因子组合成单一暴露度量。这种情况发生在许多复杂的混合物，包括多种同系物，空气污染物和独特形式的电离辐射，除其他外，在环境和职业流行病学。方法用于结合暴露的权重是最经常确定的实验动物和细胞研究。然而，从观察性研究的证据是必要的，以支持他们使用的风险分析，因为从实验研究的结果不直接转化为observational epidemiology.Results使用模拟数据，我们表明，基于比率的相对权重不能可靠地估计从观察性研究。作为一个解决这个问题的方法，我们提出了一种方法，用于估计不同的曝光的有效性的差异的基础上，他们的过剩的有效性相比，与参考exposure.Conclusions这种替代方法是很容易计算，并提供可靠的估计不同的曝光的有效性的差异。这对于使用相关措施进行政策决策的监管机构以及进行风险分析的执业流行病学家非常重要。
Objectives Mixed exposures are often combined into single exposure measures using weighting factors. This occurs for many complex mixtures in environmental and occupational epidemiology including multiple congeners, air pollutants and unique forms of ionising radiation, among others.Methods The weights used for combining exposures are most often determined from experimental animal and cellular research. However, evidence from observational research is necessary to support their use in risk analyses, since results from experimental research do not directly translate to observational epidemiology.Results Using simulated data, we show that ratio-based relative weights cannot be reliably estimated from observational research. As a solution to this problem, we propose an approach for estimating differences in effectiveness of distinct exposures based on their excess effectiveness compared with a reference exposure.Conclusions This alternative is easy to calculate and provides reliable estimates of differences in effectiveness of distinct exposures. This is important to regulatory bodies using relative measures for policy decisions, as well as practicing epidemiologists conducting risk analyses.