Calculating the "number needed to be exposed" with adjustment for confounding variables in epidemiological studies
Calculating the "number needed to be exposed" with adjustment for confounding variables in epidemiological studies
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DOI：
10.1016/s0895-4356(01)00510-8
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发表时间：
2002-05-01
影响因子：
7.2
通讯作者：
Blettner, M
中科院分区：
文献类型：
--
作者：
Bender, R;Blettner, M
需要治疗的数量（NNT）是一种流行的汇总统计量，用于描述新治疗与标准治疗或对照相比的绝对效果，涉及不良事件的风险。NNT概念可用于比较两组之间不良事件的风险;对于流行病学研究中暴露与未暴露受试者的比较，我们提出术语“需要暴露的人数”（NINE）。在随机临床试验中，NNT可以根据一个简单的2x2表计算，而在流行病学研究中，大多数应用都需要调整混杂因素的方法。我们推导出一种基于多元逻辑回归分析的方法，用于调整混杂变量的NNE的PET形点和区间估计。调整后的NNE可根据调整后的比值比（OR）和通过适当的多元logistic回归模型估计的未暴露事件率（UER）计算。由于UER依赖于混杂因素，因此调整后的NNE也随混杂变量的值而变化。提出了两种方法来考虑NNE对混杂因素值的依赖性。调整后的暴露量可以作为流行病学研究中常见结果（如OR和归因风险）的有益补充。(C)2002年爱思唯尔科技有限公司All rights reserved.
The number needed to treat (NNT) is a popular summary statistic to describe the absolute effect of a new treatment compared with a standard treatment or control concerning the risk of an adverse event. The NNT concept can be applied whenever the risk of an adverse event is compared between two groups; for the comparison of exposed with unexposed subjects in epidemiological studies, we propose the term "number needed to be exposed" (NINE). Whereas in randomized clinical trials NNT can be calculated on the basis of a simple 2x2 table, in epidemiological studies methods to adjust for confounders are required in most applications. We derive a method based upon multiple logistic regression analysis to pet-form point and interval estimation of NNE with adjustment for confounding variables. The adjusted NNE can be calculated from the adjusted odds ratio (OR) and the unexposed event rate (UER) estimated by means of an appropriate multiple logistic regression model. As UER is dependent on the confounders, the adjusted NNEs also vary with the values of the confounding variables. Two methods are proposed to take the dependence of NNE on the values of the confounders into account. The adjusted number needed to be exposed can be a useful complement to the commonly presented results in epidemiological studies, such as ORs and attributable risks. (C) 2002 Elsevier Science Inc. All rights reserved.