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Activity Number: 223 - Clinical Trials: Recent Statistical Advances for Enabling Personalized Medicine
Type: Topic Contributed
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #324064 View Presentation
Title: Efficient Approaches to Identifying Markers That Predict Treatment Effects in Randomized Clinical Trials
Author(s): James Dai* and Michael LeBlanc
Companies: Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center
Keywords: case-only ; gene-treatment interaction ; predictive marker ; relative risk
Abstract:

Retrospectively measuring markers in stored baseline samples from participants in a randomized controlled trial (RCT) provide high quality evidence as to the value of the markers for treatment selection. Originally developed for approximating gene-environment interactions in the odds ratio scale, the case-only method is recently shown to provide a consistent and efficient estimator of marker by treatment interactions and marker-specific treatment effects on the relative risk scale. The prohibitive rare-disease assumption is no longer needed and any statistical learning algorithm can be applied in the case-only fashion. Furthermore, this approach can be combined with two-stage screening methods to reduce the burden of high-dimensional testing and increase power. For example, an adaptive case-only Lasso algorithm can be developed using marginal genetic association as weight in penalty. The utility of these approaches are illustrated by an application to genetic data in the Prostate Cancer Prevention Trial (PCPT).


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

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