E-value as a Tool for Assessing Unmeasured Confounding in Observational Studies (Motivated by the Study on Prophylactic PPI Use and Acute Kidney Injury by Jing Xu et al.)

Observational studies often face challenges related to unmeasured confounding, which can threaten the validity of causal inferences. The E-value, introduced by VanderWeele and Ding (2017), offers a quantitative metric to assess how robust an observed association is to potential unmeasured confounders. This report uses a recent study by Jing Xu et al. (2025) on prophylactic proton pump inhibitor (PPI) use and new-onset acute kidney injury (AKI) in ICU patients as a case example. Despite extensive statistical adjustment, the study reported an adjusted odds ratio of 1.43 and an E-value of 2.34, indicating that an unmeasured confounder would need a strong association with both exposure and outcome to explain away the finding. This report outlines how E-value analysis complements traditional sensitivity methods, discusses dataset prerequisites for valid calculation, and presents visual tools (e.g., forest plots, sensitivity curves) that enhance interpretation. While the E-value does not account for all biases, it improves transparency in observational research and helps contextualize causal claims. As real-world datasets expand, incorporating E-value analyses can enhance the credibility of non-randomized evidence, especially in clinical and epidemiologic research where randomized trials are not feasible. Future directions include automation, integration with bias analysis, and benchmarking against known confounders.