Hidden biases in observational epidemiology: the case of unmeasured confounding.
作者信息
Ananth C V, Schisterman E F
机构信息
Department of Obstetrics and Gynecology, College of Physicians and Surgeons, Columbia University, New York, NY, USA.
Department of Epidemiology, Joseph L. Mailman School of Public Health, Columbia University, New York, NY, USA.
出版信息
BJOG. 2018 May;125(6):644-646. doi: 10.1111/1471-0528.14960. Epub 2017 Nov 17.
Abstract
摘要
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本文引用的文献
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Sensitivity Analysis in Observational Research: Introducing the E-Value.观察性研究中的敏感性分析：引入 E 值。
Ann Intern Med. 2017 Aug 15;167(4):268-274. doi: 10.7326/M16-2607. Epub 2017 Jul 11.
2
Perinatal and maternal morbidity and mortality among term singletons following midcavity operative vaginal delivery versus caesarean delivery.足月单胎经阴道手术助产与剖宫产的围产儿和产妇发病率与死亡率。
BJOG. 2018 May;125(6):693-702. doi: 10.1111/1471-0528.14820. Epub 2017 Aug 21.
3
Sensitivity Analysis Without Assumptions.无假设的敏感性分析。
Epidemiology. 2016 May;27(3):368-77. doi: 10.1097/EDE.0000000000000457.
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Conditioning on intermediates in perinatal epidemiology.围产期流行病学中对中间产物的条件作用。
Epidemiology. 2012 Jan;23(1):1-9. doi: 10.1097/EDE.0b013e31823aca5d.
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A definition of causal effect for epidemiological research.流行病学研究中因果效应的定义。
J Epidemiol Community Health. 2004 Apr;58(4):265-71. doi: 10.1136/jech.2002.006361.
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