Document Type
Article
Publication Date
1-30-2026
Abstract
PURPOSE: Our study is motivated by evaluating the role of hematopoietic cell transplantation (HCT) after chimeric antigen receptor T-cell (CAR-T) therapy for ALL, a debated topic. Because patients may receive HCT at different times after CAR-T infusion or never, HCT post-CAR-T should be considered as a time-varying covariate (TVC).
METHODS: Standard Cox models and Kaplan-Meier (KM) curves (naïve method) assume that TVC status is known and fixed at baseline, which can yield biased estimates. Landmark analysis is a popular alternative but depends on a chosen landmark time. Time-dependent (TD) Cox model is better suited for TVC although visualizing survival curves is complex. The newly proposed Smith-Zee method generates appropriate survival curves from TD Cox models.
RESULTS: To address these challenges, we developed an open-source R Shiny tool integrating multiple models (naïve Cox, landmark Cox, and TD Cox) and curves (naïve KM, landmark KM, Smith-Zee, and Extended KM) to facilitate TVC analysis. Reanalysis of post-CAR-T HCT's effect on leukemia-free survival (LFS) showed consistent results between naïve and TD Cox models, whereas landmark analyses varied by landmark time. A separate data analysis of chronic graft-versus-host disease and survival showed that substantial differences emerged across statistical methods. Simulations revealed increased bias in naïve methods when TVC changed late and minimal bias when TVC changes occurred early relative to time to events.
CONCLUSION: We recommend TD Cox models and Smith-Zee curves for robust TVC analysis. Our R Shiny tool supports standardized analyses without requiring data sharing, thereby promoting collaboration across different institutions and providing a practical tool to advance survival analysis in oncology research.
Recommended Citation
Li, Yimei; Qiao, Yang; Gao, Fei; Gauthier, Jordan; Zhang, Qiang Ed; Voutsinas, Jenna; Leisenring, Wendy; Gooley, Ted; Summers, Corinne; Hirayama, Alexandre; Turtle, Cameron; Gardner, Rebecca; Zee, Jarcy; and Wu, Qian Vicky, "Novel R Shiny Tool for Survival Analysis With Time-Varying Covariate in Oncology Studies: Overcoming Biases and Enhancing Collaboration" (2026). Wills Eye Hospital Papers. Paper 286.
https://jdc.jefferson.edu/willsfp/286
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
41616239
Language
English
Comments
This article is the author’s final published version in JCO clinical cancer informatics, Volume 10, 2026, Article number e2500225.
The published version is available at https://doi.org/10.1200/CCI-25-00225. Copyright © 2026 by American Society of Clinical Oncology.