Quantitative bias analysis methods for summary level epidemiologic data in the peer-reviewed literature: a systematic review

Abstract

AbstractObjectiveQuantitative bias analysis (QBA) methods evaluate the impact of biases arising from systematic errors on observational study results. This systematic review aimed to summarize the range and characteristics of quantitative bias analysis (QBA) methods for summary level data published in the peer-reviewed literature.Study Design and SettingWe searched MEDLINE, Embase, Scopus, and Web of Science for English-language articles describing QBA methods. For each QBA method, we recorded key characteristics, including applicable study designs, bias(es) addressed; bias parameters, and publicly available software. The study protocol was pre-registered on the Open Science Framework (https://osf.io/ue6vm/).ResultsOur search identified 10,249 records, of which 53 were articles describing 57 QBA methods for summary level data. Of the 57 QBA methods, 51 (89%) were explicitly designed for observational studies, 2 (4%) for non-randomized interventional studies, and 4 (7%) for meta-analyses. There were 29 (51%) QBA methods that addressed unmeasured confounding, 20 (35%) misclassification bias, 5 (9%) selection bias, and 3 (5%) multiple biases. 38 (67%) QBA methods were designed to generate bias-adjusted effect estimates and 18 (32%) were designed to describe how bias could explain away observed findings. 22 (39%) articles provided code or online tools to implement the QBA methods.ConclusionIn this systematic review, we identified a total of 57 QBA methods for summary level epidemiologic data published in the peer-reviewed literature. Future investigators can use this systematic review to identify different QBA methods for summary level epidemiologic data.What is New?Key findingsThis systematic review identified 57 quantitative bias analysis (QBA) methods for summary level data from observational and non-randomized interventional studies.Overall, there were 29 QBA methods that addressed unmeasured confounding, 20 that addressed misclassification bias, 5 that addressed selection bias, and 3 that addressed multiple biases.What this adds to what is known related to methods research within the field of clinical epidemiology?This systematic review provides an overview of the range and characteristics of QBA methods for summary level epidemiologic that are published in the peer-reviewed literature and that can be used by researchers within the field of clinical epidemiology.What is the implication, what should change now?This systematic review may help future investigators identify different QBA methods for summary level data. However, investigators should carefully review the original manuscripts to ensure that any assumptions are fulfilled, that the necessary bias parameters are available and accurate, and that all interpretations and conclusions are made with caution.