Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/133388
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dunne, J. | - |
dc.contributor.author | Tessema, G.A. | - |
dc.contributor.author | Ognjenovic, M. | - |
dc.contributor.author | Pereira, G. | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Annals of Epidemiology, 2021; 63:86-101 | - |
dc.identifier.issn | 1047-2797 | - |
dc.identifier.issn | 1873-2585 | - |
dc.identifier.uri | https://hdl.handle.net/2440/133388 | - |
dc.description.abstract | Purpose: 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. Methods: 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. Results: 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. Conclusions: 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. | - |
dc.description.statementofresponsibility | Jennifer Dunne, Gizachew A Tessema, Milica Ognjenovic, Gavin Pereira | - |
dc.language.iso | en | - |
dc.publisher | Elsevier | - |
dc.rights | © 2021 Elsevier Inc. All rights reserved. | - |
dc.source.uri | http://dx.doi.org/10.1016/j.annepidem.2021.07.033 | - |
dc.subject | Selection Bias; Confounding; Information Bias; Misclassification, Collider; Statistical Modelling | - |
dc.subject.mesh | Bias | - |
dc.subject.mesh | Computer Simulation | - |
dc.subject.mesh | Female | - |
dc.subject.mesh | Humans | - |
dc.subject.mesh | Pregnancy | - |
dc.title | Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/j.annepidem.2021.07.033 | - |
dc.relation.grant | http://purl.org/au-research/grants/nhmrc/1099655 | - |
dc.relation.grant | http://purl.org/au-research/grants/nhmrc/1173991 | - |
dc.relation.grant | http://purl.org/au-research/grants/nhmrc/1195716 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Tessema, G.A. [0000-0002-4784-8151] | - |
Appears in Collections: | Public Health publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.