Quantative Bias Analysis
About the Quantitative Bias Analysis (QBA) SIG
Bias—such as from exposure misclassification, confounding, or selection processes—is present to some degree in every study. QBA provides structured, quantitative tools to explore how those biases might affect results, helping researchers and decision-makers interpret findings with greater confidence.
This SIG grew out of a simple idea: while methods for bias analysis have advanced dramatically over the past 20 years, they remain underused in applied research. Our goal is to change that by connecting people, sharing tools, and learning together to make QBA a standard part of epidemiology practice.
This group connects people who are interested in applying and advancing these methods to make environmental and occupational epidemiology more transparent, credible and informative.
Membership is open to all ISEE members—students, researchers, practitioners, and policy professionals alike. Whether you’re new to QBA or already applying it, we welcome your participation and ideas as we build a collaborative community dedicated to improving how epidemiologic evidence is evaluated and used in decision-making.
What We Do
This group raises awareness of QBA methods, and fosters their application through:
Educational events - We organize webinars, workshops and conference sessions highlighting the fundamentals of QBA and its real-world applications in environmental and occupational epidemiology.
A tool and resources hub – We are compiling a growing library of recorded talks, teaching materials, and practical tools for implementing QBA in software such as Stata, R, and SAS, and make them available to all via this site.
Collaboration – We encourage discussions, mentorship, and networking for students, researchers, and professionals alike.
QBA Resources
QBA Essentials
Every epidemiologic study faces some degree of uncertainty—not just from random error, but from systematic error, or “bias.” Bias can arise from exposure misclassification, confounding, or selection processes that skew who gets into a study or how their data are measured. Quantitative Bias Analysis provides practical tools to estimate how much these biases might influence our findings, helping us see whether our conclusions still hold under realistic assumptions.
Key references to learn more about QBA
Bond JC, Fox MP, Wise LA, and Heaton, B. 2023. Quantitative Assessment of Systematic Bias: A Guide for Researchers. J. Dent Res. 102(12):1288–1292.
A step-by-step guide illustrating QBA in a real study. Great for beginners. The Appendix to the article details an applied example based on an evaluation of the published association between preconception periodontitis and time to pregnancy based on an evaluation of the published association between preconception periodontitis and time to pregnancy
Fox MP, Lash TL. On the Need for Quantitative Bias Analysis in the Peer-Review Process. Am J Epidemiol. 2017.
Argues that incorporating QBA into peer review would improve the rigor and transparency of epidemiologic science by replacing speculative discussion of bias with quantification of its likely impact on study findings.
IARC. Statistical Methods in Cancer Research Volume V: Bias Assessment in Case–Control and Cohort Studies for Hazard Identification. 2024 IARC Scientific Publication No. 17, edited by Berrington de González A, Richardson DB, Schubauer-Berigan MK.
The official IARC Monograph on Bias Assessment. Includes extensive discussion of QBA.
Lash TL, Fox MP, MacLehose RF, Maldonado G, McCandless LC, Greenland S. 2014. Good practices for quantitative bias analysis. Int J Epidemiol. 43(6):1969-85.
A clear article outlining best practices for QBA — accessible, good for beginners. Fox MP, MacLehose R, Lash TL. 2021. Applying Quantitative Bias Analysis to Epidemiologic Data. 2nd ed. Springer. Link A full textbook/guide on QBA. Great for the SIG as a “go-to” foundational reference.
Fox MP, MacLehose R, Lash TL. 2021. Applying Quantitative Bias Analysis to Epidemiologic Data. 2nd ed. Springer.
A full textbook/guide on QBA. Great for the SIG as a “go-to” foundational reference.
Webinar recordings, teaching materials, and case studies.
Past Webinars in the Series:
Quantitative Bias Analysis: The Good, the Bad, and the Ugly
Abstract:
Quantitative bias analysis encompasses all methods used to estimate the direction, magnitude, and uncertainty from non-randomized research. Many of these methods have been well known for decades but are still not routinely implemented. This talk will review the methods, their utility, where there are shortcomings, and how they are sometimes used (intentionally or unintentionally) against their best purposes.
Upcoming Webinars in the Series:
- Spring 2026 Webinar #3: TBD
- Summer 2026 Webinar #4: TBD
Get Involved
Join the SIG/Stay Connected: If you're an ISEE member, you can contact the ISEE Secretariat and the QBA SIG chair, David Miller for instructions using the form below.
Who we welcome:
Everyone—from students exploring QBA for the first time to experienced researchers, educators, and policymakers interested in strengthening the use of epidemiologic evidence in decision-making. Our hope is that this SIG becomes a home for people who are curious, collaborative, and committed to improving how we understand and communicate uncertainty in environmental health research.