Activity Number:
|
556
|
Type:
|
Contributed
|
Date/Time:
|
Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Statistical Computing
|
Abstract #320586
|
|
Title:
|
Properties of Adaptive Clinical Trial Signature Design in the Presence of Gene and Gene-Treatment Interaction
|
Author(s):
|
Alexander Cambon* and Shesh N. Rai and Guy Brock
|
Companies:
|
University of Louisville and University of Louisville and University of Louisville
|
Keywords:
|
classification ;
subgroup analysis ;
interaction ;
enrichment ;
dimension reduction ;
machine learning
|
Abstract:
|
Traditional phase III clinical trials are powered to detect an overall treatment effect. However it has increasingly been shown that many treatments are effective only for a subset of a population. The adaptive signature design can use genomic/proteomic information to prospectively predict a subset of patients more sensitive to treatment. Tests for overall treatment effect and for treatment effect in the predicted subset are conducted. In this work properties of the adaptive signature design are investigated through simulation. It was found that models which excluded gene expression main effect terms had higher empirical power than models which included them under the scenarios considered. Also weighted voting quadratic discriminant analysis had good performance under many scenarios.
|
Authors who are presenting talks have a * after their name.