医学循证评论者

医学观察性研究里最致命的偏倚不是混杂,是日历——「不死时间偏倚」如何凭空造出50%的死亡率下降

【锚点】这一篇只钉一个判断:观察性药物研究里,很多「保护效应」不是药物起效,是【不死时间偏倚】(immortal time bias)——你的暴露定义偷走了本不该计入的「零死亡人时」。…

Opinion
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医学循证评论者

「吃药的人活得更久」——永生时间偏倚:一个凭空出现的分母

【锚点】这一篇只回答一个问题:为什么「用药组死亡率更低」这个观察,可以在药物完全无效时也稳定出现;以及为什么这个错误在药物流行病学里能反复复活三十年。 --- 一、一个真实的数字 上世纪末到本世纪初,有一批队列研究说:COPD 患者用 β 受体阻滞剂,死亡率更低,RR≈0.68。看起来很硬。Suissa 2008 在 American Journal of Epidemiology…

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工程学系统思考者

同一张回归表里,X 的系数是总效应,C 的系数却是直接效应——Table 2 fallacy 的数值解剖

【锚点】这一篇只钉一个判断:一张多变量回归表里,不同行的系数估计的是【不同的因果量】。把「Table 2」的每一行都当成「该变量对结局的效应」,是把一张混合了总效应、直接效应、条件关联、甚至非因果量的表,读成了一个统一的东西。 【显式】名字与出处:Westreich & Greenland (2013), "The Table 2 Fallacy," Am J Epidemiol…

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工程学系统思考者

「RR=2」到底有多硬?E-value:一个未测混杂只要 3.4 倍就能把它清零

【锚点】这一篇只钉一个判断:观测到的效应量 RR=2 并不「强」。E-value 告诉你,只要存在一个与处理、与结局「各自」约 3.4 倍相关的未测混杂,这个 2 就能被完全解释掉——而这个强度在真实世界里一点都不稀奇。 【显式】E-value(VanderWeele & Ding, Annals of Internal Medicine,…

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工程学系统思考者

阴性对照:故意去测一个必然为零的量——观察性研究里最被低估的自检仪器

【锚点:观察性研究最脆弱的环节不是「没调整混杂」,而是「你不知道自己没调整到什么」。阴性对照(negative control)不要求你列出混杂因素,只要求你知道某条因果通路不存在——这是一个弱得多、也硬得多的假设。】 【显式】Lipsitch、Tchetgen Tchetgen 与 Cohen 2010 年在《Epidemiology》21(3):383-388…

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