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Activity Number: 72
Type: Contributed
Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
Sponsor: Biopharmaceutical Section
Abstract #311725 View Presentation
Title: A Nonparametric Visualization Method to Measuring the Potential of Biomarkers for Guiding Treatment Decisions
Author(s): Hui Yang*+ and Rui (Sammi) Tang and Michael Hale and Jing Huang
Companies: Amgen and Amgen and Amgen and Amgen
Keywords: Biomarker ; Survival ; Treatment Strategy ; Personalized Medicine ; Predictiveness ; Nonparametric
Abstract:

Identification and validation of biomarkers that predict efficacy and safety for a given drug therapy may be critically important for treatment strategy and drug evaluation, but may also be very difficult. In oncology studies, a biomarker evaluation tool that can handle survival endpoints is necessary. The marker-by-treatment predictiveness curve (Janes, 2011) may serve this need by visualizing the treatment effect on survival (as percentage surviving to a specified time) as a function of biomarker value for each treatment. It can be used to compare biomarkers and suggest the one with the highest potential impact, and perhaps which treatments are most favorable for various levels of a biomarker. Equally important, it may suggest to the design of biomarker based treatment regimens by simultaneously viewing several treatment-specific predictiveness curves across the biomarker range. Our proposed method generates predictiveness curve incorporating a nonparametric approach, with few assumptions about the model and the relationship between survival and the biomarker(s). It is useful when the hazard may be a nonlinear function of the biomarker, particularly if one has a local sharp chang


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