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Activity Number: 632 - Statistical Issues Specific the Therapeutic Areas-4
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract #328358 Presentation
Title: Evaluation of Statistical Methods for Survival Analysis with Time-Dependent Variables
Author(s): Chris Holland* and Qui Tran and Cassie Dong
Companies: Amgen and Amgen and Amgen
Keywords: survival analysis; Cox model; time-dependent covariates; immortal-time bias

In cancer trials, it is common to study the impact of time-dependent variables determined during the study follow-up (such as transplant status or the responder status on a short-term outcome) on the long-term clinical endpoints. With the naïve Cox model or the Kaplan-Meier estimate, we face the issue of the immortal-time bias when evaluating the association between the responders or transplanted patients and long-term outcomes such as overall survival. In our work, we conducted simulation studies under various scenarios to compare the naïve Cox model, landmark analysis, and the time-dependent Cox model in terms of the reduction of the immortal-time bias. The aim of the project is to use proper statistical methods to solve practical issues in survival analysis for oncology clinical trials.

Authors who are presenting talks have a * after their name.

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