On the Need for Quantitative Bias Analysis in the Peer-Review Process

Abstract

Peer review is central to the process through which epidemiologists generate evidence to inform public health and medical interventions. Reviewers thereby act as critical gatekeepers to high-quality research. They are asked to carefully consider the validity of the proposed work or research findings by paying careful attention to the methodology and critiquing the importance of the insight gained. However, although many have noted problems with the peer-review system for both manuscripts and grant submissions, few solutions have been proposed to improve the process. Quantitative bias analysis encompasses all methods used to quantify the impact of systematic error on estimates of effect in epidemiologic research. Reviewers who insist that quantitative bias analysis be incorporated into the design, conduct, presentation, and interpretation of epidemiologic research could substantially strengthen the process. In the present commentary, we demonstrate how quantitative bias analysis can be used by investigators and authors, reviewers, funding agencies, and editors. By utilizing quantitative bias analysis in the peer-review process, editors can potentially avoid unnecessary rejections, identify key areas for improvement, and improve discussion sections by shifting from speculation on the impact of sources of error to quantification of the impact those sources of bias may have had.