[Definition, concept, and practical example of information bias]

Objectives　This paper aims to elucidate the fundamental concept of "information bias" in analytical epidemiological studies. Specifically, it focuses on bias arising from covariates, an area for which educational case examples are scarce, and explains how and why such bias distorts research results. Using a real-world example, the paper examines the underlying mechanism and emphasizes the importance of precise covariate definition in study design.Methods　Information bias is defined as "a flaw in measuring exposure, covariate, or outcome variables that results in different quality (accuracy) of information between comparison groups." Based on this definition, we explain how distorted results arise and analyze information bias associated with the covariate "Study Period (SP)" in an epidemiological study using human papillomavirus vaccination data from Nagoya City. The starting point of SP differed between comparison groups, with the vaccinated group having a shorter period defined from age 12 years to the first vaccination date. Using the Nagoya data, we evaluated the impact of adjusting for SP, a covariate that meets the definition of information bias, on the study results.Results　Simulation analyses showed that bias emerged when SP was used as a covariate instead of age, which should have been adjusted for. Adjustment for SP introduced differential misclassification because the average observation period was shorter in the vaccinated group than in the non-vaccinated group, leading to distortion in the study results. Consequently, the reported odds ratios were distorted due to information bias originating from the covariate SP.Conclusion　This study demonstrates through simulation that information bias arises when covariate definitions differ between comparison groups in analytical epidemiological studies, leading to distorted results. Compared with bias related to exposure or outcome variables, information bias originating from covariates is rarely addressed in educational materials, and its mechanisms are more complex, making it less likely to be recognized. Ensuring uniform covariate definitions is crucial for valid analytical epidemiological research and warrants greater emphasis in future research and education.