Curtin School of Population Health, Curtin University, Bentley, WA, Australia.
Curtin School of Population Health, Curtin University, Bentley, WA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia.
Ann Epidemiol. 2021 Nov;63:86-101. doi: 10.1016/j.annepidem.2021.07.033. Epub 2021 Aug 9.
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.
在流行病学研究中应用模拟数据可说明和量化观察性研究中常见的各种偏倚的程度。本综述旨在回顾模拟方法在生殖和围生期流行病学中量化偏倚的应用，并评估其获得的价值。
2020 年 8 月，使用 PubMed、Medline、Embase、CINAHL 和 Scopus 对已发表的英文研究进行了检索。通过 Google 和 Google Scholar 进行灰色文献检索，并对纳入研究的参考文献进行手工检索。
本研究共纳入 39 篇论文，涵盖信息偏倚（n=14）、选择偏倚（n=14）、混杂偏倚（n=9）、保护偏倚（n=1）和衰减偏倚（n=1）。模拟数据的方法和结果报告各不相同，最近的研究中包含了因果关系图。很少有研究包括可复制的代码。
尽管自 2015 年以来，在生殖和围生期流行病学中越来越多地应用模拟方法，但总体而言，这仍是一个尚未充分探索的领域。需要进一步努力提高对模拟应用如何量化偏倚影响的认识，包括改进设计、分析和报告。这将提高生殖和围生期研究中的因果解释。
