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ShinyEvents: harmonizing longitudinal data for real-world survival estimation
Alyssa Obermayer1,2, Joshua Davis3, Divya Priyanka Talada3
1H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. Alyssa.Obermayer@Moffitt.org.
NPJ Precision Oncology
|January 12, 2026
Summary
ShinyEvents is a new web tool that analyzes patient treatment data over time. It links treatment events to survival outcomes, aiding in clinical research and data analysis.
Area of Science:
- Oncology
- Bioinformatics
- Data Science
Background:
- Longitudinal data analysis is crucial for understanding treatment outcomes.
- Existing tools struggle to integrate multilayered time-series data and link treatments to survival.
- This gap hinders comprehensive analysis of patient treatment courses.
Purpose of the Study:
- To develop ShinyEvents, a web-based framework for complex longitudinal data analysis.
- To enable integration of multilayered time-series data with survival analytics.
- To facilitate transparent and reproducible collaboration between clinicians and data scientists.
Main Methods:
- Developed ShinyEvents, a web-based framework for longitudinal data analysis.
- Implemented interactive timelines for clinical events and cohort visualizations (Sankey, Swimmer diagrams).
- Enabled inference of real-world progression-free survival (rwPFS) and survival analyses (Kaplan-Meier, Cox regression).
Main Results:
- ShinyEvents allows cohort-level analyses, including treatment clustering and endpoint assignment.
- The tool visualizes patient journeys and treatment lines effectively.
- Case study on muscle-invasive bladder cancer patients showed cisplatin and gemcitabine improved rwPFS and overall survival.
Conclusions:
- ShinyEvents offers a unified framework for integrating longitudinal real-world data with survival analytics.
- The tool supports association of treatment lines with clinical outcomes.
- ShinyEvents enhances collaborative research in oncology and data science.
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