A Novel R Shiny Tool TVCurve for Survival Analysis with Time-Varying Covariate in Oncology Studies

Oregon Convention Center 

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.

R Shiny

Time Varying Covariates

Time-dependent Cox model

Survival Curves

Visualization 

Biopharmaceutical Section