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    Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation

    Access Status
    Open access via publisher
    Authors
    Dunne, Jennifer
    Tessema, Gizachew
    Ognjenovic, Milica
    Pereira, Gavin
    Date
    2021
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Dunne, J. and Tessema, G. and Ognjenovic, M. and Pereira, G. 2021. Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation. Annals of Epidemiology. 63: pp. 86-101.
    Source Title
    Annals of Epidemiology
    DOI
    10.1016/j.annepidem.2021.07.033
    ISSN
    1047-2797
    Faculty
    Faculty of Health Sciences
    School
    Curtin School of Population Health
    Office of the Pro Vice Chancellor Health Sciences
    Funding and Sponsorship
    http://purl.org/au-research/grants/nhmrc/1099655
    http://purl.org/au-research/grants/nhmrc/1173991
    http://purl.org/au-research/grants/nhmrc/1195716
    URI
    http://hdl.handle.net/20.500.11937/93729
    Collection
    • Curtin Research Publications
    Abstract

    The application of simulated data in epidemiological studies enables the illustration and quantification of the magnitude of various types of bias commonly found in observational studies. This was a review of the application of simulation methods to the quantification of bias in reproductive and perinatal epidemiology and an assessment of value gained. A search of published studies available in English was conducted in August 2020 using PubMed, Medline, Embase, CINAHL, and Scopus. A gray literature search of Google and Google Scholar, and a hand search using the reference lists of included studies was undertaken. Thirty-nine papers were included in this study, covering information (n =14), selection (n = 14), confounding (n = 9), protection (n=1), and attenuation bias (n=1). The methods of simulating data and reporting of results varied, with more recent studies including causal diagrams. Few studies included code for replication. Although there has been an increasing application of simulation in reproductive and perinatal epidemiology since 2015, overall this remains an underexplored area. Further efforts are required to increase knowledge of how the application of simulation can quantify the influence of bias, including improved design, analysis and reporting. This will improve causal interpretation in reproductive and perinatal studies.

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