Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation - Dunne, Jennifer; Tessema, Gizachew; Ognjenovic, Milica; Pereira, Gavin (ELSEVIER SCIENCE INC, 2021)
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.