A systematic review of quantitative bias analysis applied to epidemiological research. (20th April 2021)

• Record Type: Journal Article
• Authors: Petersen, Julie M; Ranker, Lynsie R; Barnard-Mayers, Ruby; MacLehose, Richard F; Fox, Matthew P
• Is Part Of: International journal of epidemiology. Volume 50:Number 5(2021)
• Page Start: 1708
• Page End: 1730
• Publication Date: 2021-04-20
• Subjects: Epidemiologic bias -- epidemiologic study characteristics -- quantitative bias analysis -- quantitative evaluation -- systematic bias -- uncertainty; Epidemiology -- Periodicals; 614.4
• DOI: 10.1093/ije/dyab061
• Languages: English
• ISSNs: 0300-5771

Abstract

Abstract: Background: Quantitative bias analysis (QBA) measures study errors in terms of direction, magnitude and uncertainty. This systematic review aimed to describe how QBA has been applied in epidemiological research in 2006–19. Methods: We searched PubMed for English peer-reviewed studies applying QBA to real-data applications. We also included studies citing selected sources or which were identified in a previous QBA review in pharmacoepidemiology. For each study, we extracted the rationale, methodology, bias-adjusted results and interpretation and assessed factors associated with reproducibility. Results: Of the 238 studies, the majority were embedded within papers whose main inferences were drawn from conventional approaches as secondary (sensitivity) analyses to quantity-specific biases (52%) or to assess the extent of bias required to shift the point estimate to the null (25%); 10% were standalone papers. The most common approach was probabilistic (57%). Misclassification was modelled in 57%, uncontrolled confounder(s) in 40% and selection bias in 17%. Most did not consider multiple biases or correlations between errors. When specified, bias parameters came from the literature (48%) more often than internal validation studies (29%). The majority (60%) of analyses resulted in >10% change from the conventional point estimate; however, most investigators (63%) did not alter their original interpretation. Degree of reproducibility related to inclusion of code, formulas, sensitivity analyses and supplementary materials, as well as the QBA rationale. Conclusions: QBA applications were rare though increased over time. Future investigators should reference good practices and include details to promote transparency and to serve as a reference for other researchers.