分布相互作用:使用发生率比值比评估相互作用时的解释问题。

Epidemiologic perspectives & innovations : EP+I Pub Date : 2005-03-03 eCollection Date: 2005-01-01 DOI:10.1186/1742-5573-2-1
Ulka B Campbell, Nicolle M Gatto, Sharon Schwartz
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引用次数: 26

摘要

众所周知,发病率比值比近似于所研究疾病罕见时的风险比,但随着疾病越来越常见,其风险比就会越来越高估。然而,在评估相互作用时,即使疾病罕见,发生率优势比也不能近似于风险比。我们使用术语“分布相互作用”来指在使用发生率比值比时出现的相互作用,而在使用风险比时没有出现或出现的程度较低。由这种差异引起的解释问题可能对流行病学研究产生重要影响。因此,量化相互作用优势比和相互作用风险比之间的关系是必要的。在本文中,我们提供了一个公式来量化发生率优势比和风险比之间的差异,当它们被用于估计乘数尺度上的效果修正时。利用这个公式,我们检验了这两个估计发散的条件。此外,我们将讨论扩展到使用发生率比值比在加性尺度上评估效果改变的含义。最后,我们用文献中的一个例子来说明分布相互作用是如何产生的以及它所引起的问题。每当结果变量的风险不可忽略时,分布相互作用是可能的。即使这种疾病很罕见(例如,患病风险低于5%),情况也是如此。因此,在以加性或乘性尺度评估相互作用时,应谨慎地解释基于发生率比值比的相互作用估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Distributional interaction: Interpretational problems when using incidence odds ratios to assess interaction.

Distributional interaction: Interpretational problems when using incidence odds ratios to assess interaction.

Distributional interaction: Interpretational problems when using incidence odds ratios to assess interaction.

Distributional interaction: Interpretational problems when using incidence odds ratios to assess interaction.

It is well known that the incidence odds ratio approximates the risk ratio when the disease of interest is rare, but increasingly overestimates the risk ratio as the disease becomes more common. However when assessing interaction, incidence odds ratios may not approximate risk ratios even when the disease is rare. We use the term "distributional interaction" to refer to interaction that appears when using incidence odds ratios that does not appear, or appears to a lesser degree, when using risk ratios. The interpretational problems that arise from this discrepancy can have important implications in epidemiologic research. Therefore, quantification of the relationship between the interaction odds ratio and the interaction risk ratio is warranted. In this paper, we provide a formula to quantify the differences between incidence odds ratios and risk ratios when they are used to estimate effect modification on a multiplicative scale. Using this formula, we examine the conditions under which these two estimates diverge. Furthermore, we expand this discussion to the implications of using incidence odds ratios to assess effect modification on an additive scale. Finally, we illustrate how distributional interaction arises and the problems that it causes using an example from the literature. Whenever the risk of the outcome variable is non-negligible, distributional interaction is possible. This is true even when the disease is rare (e.g., disease risk is less than 5%). Therefore, when assessing interaction on either an additive or multiplicative scale, caution should be taken in interpreting interaction estimates based on incidence odds ratios.

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