Quantifying the impact of unmeasured confounding in observational studies with the E value

被引：17
|
作者：
Gaster, Tobias [1 ]
Eggertsen, Christine Marie [1 ]
Stovring, Henrik [2 ,3 ]
Ehrenstein, Vera [4 ,5 ]
Petersen, Irene [4 ,5 ,6 ]
机构：
[1] Aarhus Univ, Aarhus, Denmark
[2] Steno Diabet Ctr Aarhus, Aarhus, Denmark
[3] Univ Southern Denmark, Clin Pharmacol Pharm & Environm Med, Odense, Denmark
[4] Aarhus Univ, Dept Clin Epidemiol, DK-8000 Aarhus, Denmark
[5] Aarhus Univ Hosp, Aarhus, Denmark
[6] UCL, Dept Primary Care & Populat Hlth, London, England
来源：
BMJ MEDICINE | 2023年 / 2卷 / 01期
关键词：
Pregnancy complications; Epidemiology; Obstetrics; SEROTONIN REUPTAKE INHIBITORS; SENSITIVITY-ANALYSIS; MISCARRIAGE; PREGNANCY; RISK;
D O I：
10.1136/bmjmed-2022-000366
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
The E value method deals with unmeasured confounding, a key source of bias in observational studies. The E value method is described and its use is shown in a worked example of a meta-analysis examining the association between the use of antidepressants in pregnancy and the risk of miscarriage.
引用
收藏
页数：5
相关论文
共 50 条
  • [1] Using the E-Value to Assess the Potential Effect of Unmeasured Confounding in Observational Studies
    Haneuse, Sebastien
    VanderWeele, Tyler J.
    Arterburn, David
    JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2019, 321 (06): : 602 - 603
  • [2] Conducting sensitivity analysis for unmeasured confounding in observational studies using E-values: The evalue package
    Linden, Ariel
    Mathur, Maya B.
    VanderWeele, Tyler J.
    STATA JOURNAL, 2020, 20 (01) : 162 - 175
  • [3] A note on a sensitivity analysis for unmeasured confounding, and the related E-value
    Sjolander, Arvid
    JOURNAL OF CAUSAL INFERENCE, 2020, 8 (01) : 229 - 248
  • [4] Assess the Application of the E-Value in the Unmeasured Confounder Evaluation of Observational Pharmaceutical Studies
    Huang, Lihong
    Ma, Jianbing
    Qiu, Xiaochun
    Suo, Tao
    SCIENTIFIC PROGRAMMING, 2021, 2021
  • [5] Hidden biases in observational epidemiology: the case of unmeasured confounding
    Ananth, C. V.
    Schisterman, E. F.
    BJOG-AN INTERNATIONAL JOURNAL OF OBSTETRICS AND GYNAECOLOGY, 2018, 125 (06) : 644 - 646
  • [6] Assessing the impact of unmeasured confounding for binary outcomes using confounding functions
    Kasza, Jessica
    Wolfe, Rory
    Schuster, Tibor
    INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2017, 46 (04) : 1303 - 1311
  • [7] Sensitivity analyses to estimate the potential impact of unmeasured confounding in causal research
    Groenwold, Rolf H. H.
    Nelson, David B.
    Nichol, Kristin L.
    Hoes, Arno W.
    Hak, Eelko
    INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2010, 39 (01) : 107 - 117
  • [8] How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies
    Barrowman, Michael Andrew
    Peek, Niels
    Lambie, Mark
    Martin, Glen Philip
    Sperrin, Matthew
    BMC MEDICAL RESEARCH METHODOLOGY, 2019, 19 (1)
  • [9] Meta-Analysis of Observational Studies with Unmeasured Confounders
    McCandless, Lawrence C.
    INTERNATIONAL JOURNAL OF BIOSTATISTICS, 2012, 8 (02)
  • [10] Navigating Unmeasured Confounding in Nonexperimental Psychological Research: A Practical Guide to Computing and Interpreting E-Value
    Bi, Kaiwen
    Merrin, Gabriel J.
    Li, Tianyu
    Sun, Xianlin
    Chai, Yi
    Lu, Zekai
    Chen, Mark Shuquan
    ADVANCES IN METHODS AND PRACTICES IN PSYCHOLOGICAL SCIENCE, 2025, 8 (02)
12345