Statistics and Data Science Seminar
Dr. Mandy Jin
AbbVie Inc.
Methods for Informative Censoring in Time-to-Event Data Analysis
Abstract: In oncology clinical trials, subjects prematurely discontinuing from the assigned treatment prior to experiencing an event of interest are often handled by noninformative censoring under censor-at random assumption. Such methods can be challenged with respect to the robustness of the ignorable or noninformative censoring and sensitivity analyses using informative censoring are often required.
In a recently published article (Jin and Fang, 2024), reference-based methods (including Jump to Reference and Copy Reference) and tipping point analysis for time-to-event data with possibly informative censoring were proposed. These are novel methods to fit the gap in literature for time-to-event analysis with applications in oncology clinical trials. We will describe and facilitate the implementation of these methods in this presentation.
Illustrative examples are provided to demonstrate the reference-based methods and tipping point analysis.
Wednesday October 30, 2024 at 4:00 PM in Zoom