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A cautionary note on a recently proposed sensitivity analysis for unmeasured confounding.

作者信息

Ciocănea-Teodorescu Iuliana, Sjölander Arvid

机构信息

Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Solna, Sweden.

出版信息

Int J Epidemiol. 2021 Jul 9;50(3):711-716. doi: 10.1093/ije/dyaa258.

DOI:10.1093/ije/dyaa258
PMID:33367797
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8271188/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/addc/8271188/35828e40076a/dyaa258f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/addc/8271188/35828e40076a/dyaa258f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/addc/8271188/35828e40076a/dyaa258f1.jpg

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本文引用的文献

1
Assessing the impact of unmeasured confounding for binary outcomes using confounding functions.使用混杂函数评估二分类结局中未测量混杂的影响。
Int J Epidemiol. 2017 Aug 1;46(4):1303-1311. doi: 10.1093/ije/dyx023.
2
Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders.用于一般结局、处理和混杂因素的未测量混杂敏感性分析的偏倚公式。
Epidemiology. 2011 Jan;22(1):42-52. doi: 10.1097/EDE.0b013e3181f74493.
3
On the consistency rule in causal inference: axiom, definition, assumption, or theorem?
因果推断中的一致性规则：公理、定义、假设还是定理？
Epidemiology. 2010 Nov;21(6):872-5. doi: 10.1097/EDE.0b013e3181f5d3fd.
4
Bounding causal effects under uncontrolled confounding using counterfactuals.使用反事实方法在未控制混杂因素的情况下界定因果效应。
Epidemiology. 2005 Jul;16(4):548-55. doi: 10.1097/01.ede.0000166500.23446.53.
5
Marginal structural models and causal inference in epidemiology.边缘结构模型与流行病学中的因果推断
Epidemiology. 2000 Sep;11(5):550-60. doi: 10.1097/00001648-200009000-00011.