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本文引用的文献

1
The Importance of Making Assumptions in Bias Analysis.在偏差分析中进行假设的重要性。
Epidemiology. 2021 Sep 1;32(5):617-624. doi: 10.1097/EDE.0000000000001381.
2
Multiple-bias Sensitivity Analysis Using Bounds.基于界的多重偏差敏感性分析。
Epidemiology. 2021 Sep 1;32(5):625-634. doi: 10.1097/EDE.0000000000001380.
3
Invited Commentary: Dealing With the Inevitable Deficiencies of Bias Analysis-and All Analyses.特邀评论：应对偏倚分析——以及所有分析——不可避免的缺陷。
Am J Epidemiol. 2021 Aug 1;190(8):1617-1621. doi: 10.1093/aje/kwab069.
4
Commentary: Continuing the E-value's post-publication peer review.评论：延续E值发表后的同行评审。
Int J Epidemiol. 2020 Oct 1;49(5):1497-1500. doi: 10.1093/ije/dyaa097.
5
Sensitivity analysis for publication bias in meta-analyses.Meta分析中发表偏倚的敏感性分析。
J R Stat Soc Ser C Appl Stat. 2020 Nov;69(5):1091-1119. doi: 10.1111/rssc.12440. Epub 2020 Aug 28.
6
Sensitivity Analysis for Unmeasured Confounding in Meta-Analyses.Meta分析中未测量混杂因素的敏感性分析
J Am Stat Assoc. 2020;115(529):163-172. doi: 10.1080/01621459.2018.1529598. Epub 2019 Apr 30.
7
Commentary: An argument against E-values for assessing the plausibility that an association could be explained away by residual confounding.评论：反对使用E值来评估关联是否可能被残余混杂因素解释掉的合理性。
Int J Epidemiol. 2020 Oct 1;49(5):1501-1503. doi: 10.1093/ije/dyaa095.
8
E Values and Incidence Density Sampling.E值与发病密度抽样
Epidemiology. 2020 Nov;31(6):e51-e52. doi: 10.1097/EDE.0000000000001238.
9
Commentary: Developing best-practice guidelines for the reporting of E-values.评论：制定E值报告的最佳实践指南。
Int J Epidemiol. 2020 Oct 1;49(5):1495-1497. doi: 10.1093/ije/dyaa094.
10
Commentary: The value of E-values and why they are not enough.评论：E值的价值以及为何它们并不足够。
Int J Epidemiol. 2020 Oct 1;49(5):1505-1506. doi: 10.1093/ije/dyaa093.

Are Greenland, Ioannidis and Poole opposed to the Cornfield conditions? A defence of the E-value.

作者信息

VanderWeele Tyler J

出版信息

Int J Epidemiol. 2022 May 9;51(2):364-371. doi: 10.1093/ije/dyab218.

DOI:10.1093/ije/dyab218
PMID:34643669
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9082787/
Abstract
摘要