Abstract
Longitudinal data analysis of the patient’s treatment course is critical to uncovering variables that influence outcomes. However, existing tools have significant limitations in integrating multilayered time-series data, particularly in linking treatment events with survival outcomes. Here, we developed ShinyEvents, a web-based framework for complex longitudinal data analysis. ShinyEvents allows users to upload data and generate interactive timelines of clinical events, enabling cohort-level analyses such as treatment clustering and endpoint assignment. It also provides informative cohort visualizations, such as a Sankey diagram of the treatment line and a Swimmer diagram of the clinical course. Finally, 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. With these features, the tool can then associate treatment lines with clinical outcomes. As a case study, we analyzed Moffitt patients with muscle-invasive bladder cancer treated with neoadjuvant chemotherapy followed by surgery. Patients treated with cisplatin and gemcitabine exhibited more favorable rwPFS and overall survival, which is consistent with prior reports. Altogether, ShinyEvents provides a unified framework for integrating longitudinal real-world data with survival analytics, fostering transparent and reproducible collaboration between clinicians and data scientists. A live demo is available at https://shawlab-moffitt.shinyapps.io/shinyevents/.
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Data availability
Software code has been deposited inside the Zenodo repository: https://doi.org/10.5281/zenodo.16527381.
References
Verkerk, K. & Voest, E. E. Generating and using real-world data: A worthwhile uphill battle. Cell 187, 1636–1650 (2024).
Liu, F. & Panagiotakos, D. Real-world data: a brief review of the methods, applications, challenges and opportunities. BMC Med. Res. Methodol. 22, 287 (2022).
Ramsey, S. D., Onar-Thomas, A. & Wheeler, S. B. Real-World Database Studies in Oncology: A Call for Standards. J. Clin. Oncol. 42, 977–980 (2024).
Soupir, A. et al. Genomic, transcriptomic, and immunogenomic landscape of over 1300 sarcomas of diverse histology subtypes. Nat. Commun. 16, 4206 (2025).
Park, M. A. et al. Molecular Pathway and immune profile analysis of IPMN-derived versus PanIN-derived pancreatic ductal adenocarcinomas. Int. J. Mol. Sci. 25, https://doi.org/10.3390/ijms252313164 (2024).
Wang, X. et al. Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors. medRxiv https://doi.org/10.1101/2024.07.19.24310726 (2024).
Eule, C. J. et al. Clinical and genomic features of patients with renal cell carcinoma and advanced chronic kidney disease: analysis of a multi-institutional database. Cancers. 16, https://doi.org/10.3390/cancers16101920 (2024).
Demetriou, A. N. et al. Profiling the molecular and clinical landscape of glioblastoma utilizing the Oncology Research Information Exchange Network brain cancer database. Neurooncol. Adv. 6, vdae046 (2024).
Castellanos, E. H., Wittmershaus, B. K. & Chandwani, S. Raising the bar for real-world data in oncology: approaches to quality across multiple dimensions. JCO Clin. Cancer Inf. 8, e2300046 (2024).
Consortium, A. P. G. AACR Project GENIE: Powering Precision Medicine through an International Consortium. Cancer Discov. 7, 818–831 (2017).
de Bruijn, I. et al. Analysis and visualization of longitudinal genomic and clinical data from the AACR Project GENIE Biopharma Collaborative in cBioPortal. Cancer Res. 83, 3861–3867 (2023).
Harbig, T. A. et al. OncoThreads: visualization of large-scale longitudinal cancer molecular data. Bioinformatics 37, i59–i66 (2021).
Del Gaizo, J., Catchpole, K. R. & Alekseyenko, A. V. Research and Exploratory Analysis Driven-Time-data Visualization (read-tv) software. JAMIA Open 4, ooab007 (2021).
Goedhart, J. PlotTwist: A web app for plotting and annotating continuous data. PLoS Biol. 18, e3000581 (2020).
Espinosa-Carrasco, J. et al. Pergola-web: a web server for the visualization and analysis of longitudinal behavioral data using repurposed genomics tools and standards. Nucleic Acids Res. 47, W600–W604 (2019).
Maheux, E. et al. Forecasting individual progression trajectories in Alzheimer’s disease. Nat. Commun. 14, 761 (2023).
Nguena Nguefack, H. L. et al. Trajectory modelling techniques useful to epidemiological research: a comparative narrative review of approaches. Clin. Epidemiol. 12, 1205–1222 (2020).
Johnson, K. B. et al. Precision medicine, AI, and the future of personalized health care. Clin. Transl. Sci. 14, 86–93 (2021).
Choudhury, N. J. et al. The GENIE BPC NSCLC Cohort: a real-world repository integrating standardized clinical and genomic data for 1846 patients with non-small cell lung cancer. Clin. Cancer Res. 29, 3418–3428 (2023).
Obermayer, A. N. et al. PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing. BMC Bioinforma. 24, 266 (2023).
Einerhand, S. M. H. et al. Multicenter evaluation of neoadjuvant and induction gemcitabine-carboplatin versus gemcitabine-cisplatin followed by radical cystectomy for muscle-invasive bladder cancer. World J. Urol. 40, 2707–2715 (2022).
Turchioe, M. R. et al. A systematic review of patient-facing visualizations of personal health data. Appl Clin. Inf. 10, 751–770 (2019).
Avila, M. & Meric-Bernstam, F. Next-generation sequencing for the general cancer patient. Clin. Adv. Hematol. Oncol. 17, 447–454 (2019).
Stahlberg, E. A. et al. Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation. Front. Digit. Health 4, 1007784 (2022).
Altman, R. B. The interactions between clinical informatics and bioinformatics: a case study. J. Am. Med. Inf. Assoc. 7, 439–443 (2000).
Martin-Sanchez, F. et al. Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care. J. Biomed. Inf. 37, 30–42 (2004).
Alowais, S. A. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med. Educ. 23, 689 (2023).
Acknowledgements
This work has been supported in part by the Biostatistics and Bioinformatics Shared Resource at the Moffitt Cancer Center (NCI P30 CA076292), the Moffitt Cancer Center Department of Biostatistics and Bioinformatics Pilot Project (T.I.S.). Funding for this project was provided by the Department of Defense, Grant No. HT94252510691 (T.I.S., B.M.), Florida Department of Health, Grant No. MOAAX (D.C.), and the National Institute of Health, Grant No. R21 CA286417-01 (D.C.), NIH R01CA293755 (T.I.S), and Grant No. T32 CA233399-04 (J.D., X.W., T.I.S.). We thank Drs. Mitchell Hayes, Kendrick Yim, and Dae Won Kim for their valuable suggestions and comments about our tool. The authors thank all the ORIEN AVATAR Collaborative Members, Dale Hedges, Michael Radmacher, and Phaedra Agius from Aster’s Insight for their support on the project. The authors would like to acknowledge the American Association for Cancer Research and its financial and material support in the development of the AACR Project GENIE registry, as well as members of the consortium for their commitment to data sharing. Interpretations are the responsibility of the study authors.
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Contribution: A.O., T.I.S., led the software development and testing. J.D., D.P.T., M.T., D.S., D.D., S.E., V.Y., X.W., A.T., J.G., D.C., G.D.G., contributed to the software development and testing; B.J.M., A.A.T., S.G., J.M., R.L., R.R.P., and G.D.G., provided guidance on the clinical interpretation; V.Y., D.C., provided guidance on statistical analysis. A.T., R.J.R., M.L.C., R.L., G.D.G. provided patient samples and clinical data. A.O., G.D.G., D.C., and T.I.S. designed the study, analyzed data, and wrote the manuscript; A.T., G.D.G., D.C., and T.I.S. oversaw the study. All authors assisted in the preparation of the manuscript.
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G.D.G., T.I.S., and D.C. have a patent pending based on the submitted work.
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Obermayer, A., Davis, J., Talada, D.P. et al. ShinyEvents: harmonizing longitudinal data for real-world survival estimation. npj Precis. Onc. (2026). https://doi.org/10.1038/s41698-025-01212-0
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DOI: https://doi.org/10.1038/s41698-025-01212-0
