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PHS Monday Seminar: Tyler VanderWeele, PhD- “Sensitivity Analysis in Observational Research: Introducing the E-Value”
February 22, 2021 @ 12:00 pm - 1:00 pm
John L. Loeb and Frances Lehman Loeb Professor of Epidemiology
Departments of Epidemiology and Biostatistics
Harvard T.H. Chan School of Public Health
Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the “E-value,” which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment–outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
Tyler J. VanderWeele, Ph.D., is the John L. Loeb and Frances Lehman Loeb Professor of Epidemiology in the Departments of Epidemiology and Biostatistics at the Harvard T.H. Chan School of Public Health, and Director of the Human Flourishing Program and Co-Director of the Initiative on Health, Religion and Spirituality at Harvard University. He holds degrees from the University of Oxford, University of Pennsylvania, and Harvard University in mathematics, philosophy, theology, finance, and biostatistics. His methodological research is focused on theory and methods for distinguishing between association and causation in the biomedical and social sciences and, more recently, on psychosocial measurement theory. His empirical research spans psychiatric and social epidemiology; the science of happiness and flourishing; and the study of religion and health. He is the recipient of the 2017 Presidents’ Award from the Committee of Presidents of Statistical Societies (COPSS). He has published over three hundred papers in peer-reviewed journals; and is author of the books Explanation in Causal Inference (2015), Modern Epidemiology (2021), and Measuring Well-Being (2021).