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Abstract Details

Activity Number: 570
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #305665
Title: Selecting Biomarkers for Personalized Medicine in Randomized Clinical Trials via Statistical Learning Methods
Author(s): Soyeon Kim*+ and Jack Lee, Ph.D.
Companies: Rice University/MD Anderson Cancer Center and MD Anderson Cancer Center
Address: 2601 Bellefontaine st #B201, Houston, TX, 77025, United States
Keywords: Biomarker ; targeted treatment ; LASSO ; SCAD ; clinical trial ; statistical learning

Targeted treatments are developed to offer more effective treatments than the standard systemic treatments for patients with specific biomarker profiles. Since this benefit may not be extended to all patients, the goals are to select biomarkers which identify patients likely to benefit from the targeted treatment and to predict treatment response. The number of candidate biomarkers is usually much larger than the number of patients, selecting biomarkers and predicting response are challenging. We considered randomized clinical trials to evaluate one or more targeted treatments. We compared several statistical learning methods such as forward-stepwise selection for logistic regression with the AIC and BIC criteria, LASSO, smoothly clipped absolute deviation(SCAD), etc. for predicting a binary response and selecting biomarkers. Simulation studies show the pros and cons of different methods. With proper choice of the method and tuning parameters, we can correctly test the treatment efficacy and identify import biomarkers.

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