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Activity Number: 165
Type: Topic Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #319465 View Presentation
Title: Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker
Author(s): Jaya M. Satagopan* and Alexia Iasonos
Companies: Memorial Sloan Kettering Cancer Center and Memorial Sloan Kettering Cancer Center
Keywords: survival probability ; treatment option ; differential treatment benefit ; biomarker-treatment interaction ; clinical trial

There is an increased interest in finding predictive biomarkers that can guide treatment options for both mutation carriers and non-carriers. The statistical assessment of variation in treatment benefit (TB) according to the biomarker carrier status (i.e., differential TB) plays an important role in evaluating predictive biomarkers. For time to event endpoints, the hazard ratio (HR) for interaction between treatment and a biomarker from a Proportional Hazards regression model is commonly used as a measure of differential TB. While this can be easily obtained using available statistical software packages, the interpretation of HR is not straightforward. Therefore, we propose different summary measures of differential TB for evaluating a predictive biomarker based on clinically useful functions of survival probabilities. We examine the operating characteristics of our proposed measures, illustrate their use and interpretation using data from completed clinical trials, and provide suggestions for a practically useful measure.

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

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