The mockery that confounds better treatment of confounding in epidemiology: The change in estimate fallacy

Igor Burstyn
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引用次数: 0

Abstract

Confounding is one of the most infamous bugbears of epidemiology, used by some to dismiss the field's utility outright. The subject has received considerable attention from epidemiologists and the field boasts a remarkable arsenal for addressing the issue. However, it appears that there are still misconceptions about how to identify variables that cause confounding (a lack of exchangeability) in epidemiologic practice. In this commentary, I examine whether analysis of the properties of change-in-estimate method for identification of confounding, exemplified by two highly cited papers, has been appropriately cited in published reports and whether it was utilized to improve epidemiologic practice. I conclude that the myth that a change-in-estimate criterion of 10 % is legitimate for identifying confounding persists in epidemiological practice, despite having been discredited by several independent research groups decades ago. Speculations on possible solutions to this problem are offered, but my work's main contribution is identification of a problem of how methodological advances in epidemiology may be misapplied. There currently do not exist any universal criteria for identification of confounding! “Citation without representation” or biased presentation of conclusions of methodological research may be pervasive.
在流行病学中更好地处理混杂因素的嘲弄:估计值变化谬误
混杂是流行病学最臭名昭著的问题之一,有些人利用它来彻底否定该领域的实用性。流行病学家对这一问题给予了极大关注,该领域也拥有解决这一问题的强大武器。然而,在流行病学实践中,对于如何识别导致混杂(缺乏可交换性)的变量,似乎仍存在误解。在这篇评论中,我将以两篇引用率很高的论文为例,探讨在发表的报告中是否适当引用了用于识别混杂因素的估计值变化法的特性分析,以及是否利用该方法改进了流行病学实践。我的结论是,尽管数十年前就有几个独立的研究小组否定了 10% 的估计值变化标准,但流行病学实践中仍然存在着这样一个神话,即估计值变化 10% 是识别混杂因素的合法标准。我对这一问题的可能解决方案进行了推测,但我的工作的主要贡献在于发现了流行病学方法论的进步可能被误用的问题。目前还不存在任何通用的混杂识别标准!"没有代表性的引用 "或对方法学研究结论的偏颇表述可能普遍存在。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Global Epidemiology
Global Epidemiology Medicine-Infectious Diseases
CiteScore
5.00
自引率
0.00%
发文量
22
审稿时长
39 days
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