Background: <p>Analyzing time-to-event data, such as cancer patient survival time, is a central task in survival analysis. Numerous modeling methods and inference strategies have been developed for various application settings, where the primary goal is to assess the relationship between survival time and covariates. However, the applicability of existing approaches is often hindered by two major challenges. First, covariates (e.g., gene expression levels) typically exhibit complex network structures. Second, they are prone to measurement error, which can substantially bias inference if ignored.</p> Results: <p>To address these challenges, we developed the R package SurvGME (Survival analysis with Graphical and Measurement Error models). The package provides a comprehensive framework for survival analysis in the presence of both graphical dependence structures and measurement error.</p> Conclusions: <p>It supports a range of commonly used survival models, and its utility and performance are illustrated using a breast cancer dataset.</p>