ShinyEvents：协调真实世界纵向数据以获得临床洞见和生存分析

Background : Harmonizing patient longitudinal data is critical to uncovering variables and events that can influence outcomes or molecular data, yet existing tools have significant limitations in integrating multilayered time-series data, particularly in linking treatment events with survival outcomes. Due to their observational nature, real-world data (RWD) can be comprehensive and heterogeneous, posing a challenge when visualizing and interpreting the data. We developed ShinyEvents, an open-source tool and application to facilitate interaction and exploration of longitudinal data, which we demonstrate in the application of the AACR Project GENIE a global consortium that pools real-world cancer genomic and clinical data to advance precision oncology. Methods : ShinyEvents is a web-based framework that allows users to upload longitudinal data and generate interactive patient timelines to view clinical events and perform cohort-level analyses through treatment clustering and endpoint assignment. The tool provides informative cohort visualizations, such as a Sankey diagram of the treatment line, swimmer diagrams of the clinical course and treatment duration, as well as heatmaps to view unsupervised clustering on patient treatments. Our tool can infer real-world progression-free survival (rwPFS) based on user-defined endpoints and perform Kaplan-Meier and Cox proportional hazards regression analysis. We incorporated the AACR Project GENIE data on non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) into a dedicated wed instance to visualize and interact with the data. The application is publicly accessible at the following link: https://shawlab-moffitt.shinyapps.io/ShinyEvents_AACR_GENIE/. Conclusions : ShinyEvents provides a unified framework integrating longitudinal real-world data with survival analytics to facilitate transparent and reproducible collaboration between clinicians and data scientists. Based on the GENIE data, the tool is able to provide dynamic longitudinal visualization on the patient treatment regimen and relate back to the molecular data by identifying complexities surrounding sample collection in relation to treatment regimens. This standardized approach to RWD analysis will facilitate additional collaboration across the global GENIE network.