Abstract Details
Activity Number:
|
377
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract - #309832 |
Title:
|
Model-Based Classifications of High-Throughput Data Review, Design, and Application to a Cancer Clinical Study
|
Author(s):
|
AC Cambon*+ and Shesh Nath Rai
|
Companies:
|
Department of Bioinformatics and Biostatistics, University of Louisville and University of Louisville
|
Keywords:
|
classification ;
dimension reduction ;
sample size planning ;
high thoughput data ;
predictive modeling ;
machine learning
|
Abstract:
|
It is now widely recognized that many treatments for cancers such as melanoma are effective for only a subset of a population. However clinical studies for cancer treatments are more often powered to detect an overall treatment effect. In this study, parametric high throughput classification methods which could be used in a clinical study to identify a subset of patients more sensitive to treatment or more prone to relapse are reviewed. Methods such as logistic classification, linear discriminant analysis, and ROC regression, along with appropriate dimension reduction methods, are discussed. Approaches to sample size planning which take into account high throughput data and dimension reduction are also reviewed.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.