Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon--the reversal paradox.
Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon--the reversal paradox.
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辛普森的悖论，勋爵的悖论和抑制作用是相同的现象 - 逆转悖论。
DOI：
10.1186/1742-7622-5-2
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发表时间：
2008-01-22
影响因子：
2.3
通讯作者：
Gilthorpe, Mark S
中科院分区：
文献类型：
--
作者：
Tu, Yu-Kang;Gunnell, David;Gilthorpe, Mark S
本文讨论了流行病学研究中普遍存在的三个统计悖论：辛普森悖论、洛德悖论和抑制。这些悖论对于解释观察性研究的证据具有重要意义。本文使用假设情景来说明这三种悖论是一种现象的不同表现——逆转悖论——取决于结果和解释变量是绝对的、连续的还是两者的组合；这使得任何一个国家面临的问题和补救措施对所有三个国家来说都是相似的。虽然这三种统计悖论发生在不同类型的变量中，但它们具有相同的特征：当另一个变量在统计上受到控制时，两个变量之间的关联可以被逆转、减弱或增强。理解这些悖论背后的概念和理论，有助于深入了解一些有争议或相互矛盾的研究结果。这些悖论表明，先验知识和潜在的因果理论在流行病学数据的统计建模中起着重要作用，其中不正确地使用统计模型可能产生一致的、可复制的、但错误的结果。
This article discusses three statistical paradoxes that pervade epidemiological research: Simpson's paradox, Lord's paradox, and suppression. These paradoxes have important implications for the interpretation of evidence from observational studies. This article uses hypothetical scenarios to illustrate how the three paradoxes are different manifestations of one phenomenon – the reversal paradox – depending on whether the outcome and explanatory variables are categorical, continuous or a combination of both; this renders the issues and remedies for any one to be similar for all three. Although the three statistical paradoxes occur in different types of variables, they share the same characteristic: the association between two variables can be reversed, diminished, or enhanced when another variable is statistically controlled for. Understanding the concepts and theory behind these paradoxes provides insights into some controversial or contradictory research findings. These paradoxes show that prior knowledge and underlying causal theory play an important role in the statistical modelling of epidemiological data, where incorrect use of statistical models might produce consistent, replicable, yet erroneous results.