Abstract Number:
2209
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Yimei Li (1), Yang Qiao (2), Fei Gao (3), Jordan Gauthier (4), Qiang Zhang (5), Jarcy Zee (1)
Institutions:
(1) University of Pennsylvania, N/A, (2) Iowa State University, N/A, (3) Fred Hutchinson Cancer Research Center, N/A, (4) Fred Hutch, N/A, (5) Wills Eye Hospital, N/A
Co-Author(s):
Fei Gao
Fred Hutchinson Cancer Research Center
First Author:
Presenting Author:
Abstract Text:
The study of time-varying covariates (TVCs) gains attention in both statistical and medical fields. An example of a TVC is the receipt of hematopoietic cell transplantation (HCT) after CAR-T infusion, as patients may receive HCT after infusion, or not at all. The standard Cox model and Kaplan-Meier (KM) curve (Naïve method) may introduce "immortal time bias" since they assume TVC status known at baseline. Landmark analysis and time-dependent (TD) Cox model is two alternatives, but visualization of survival curves remains challenging. A novel visualization, Smith-Zee, based on TD Cox model, addresses this issue by mimicking new patients with TVC status change at different times, which overcomes drawbacks of the Naïve and Landmark methods. In this study, we developed a novel R Shiny tool called TVCurveTM to address these challenges and TVCurveTM incorporates various models: Naïve Cox, landmark Cox, and the TD Cox, along with multiple survival curves such as Naïve KM, Landmark KM, and Smith-Zee. Our tool TVCurve breaks collaboration barriers since it does not require data sharing between institutions but ensures standardized analyses across diverse datasets.
Keywords:
R Shiny|Time Varying Covariates|Time-dependent Cox model |Survival Curves|Visualization|
Sponsors:
Biopharmaceutical Section
Tracks:
Statistical issues specific to therapeutic areas
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