|
Activity Number:
|
184
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Tuesday, August 10, 2004 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Statistics in Epidemiology
|
| Abstract - #300406 |
|
Title:
|
Two-stage Designs for Population-based Association Studies
|
|
Author(s):
|
Jaya M. Satagopan*+ and E. S. Venkatraman and Colin B. Begg
|
|
Companies:
|
Memorial Sloan-Kettering Cancer Center and Memorial Sloan-Kettering Cancer Center and Memorial Sloan-Kettering Cancer Center
|
|
Address:
|
Department of Epidemiology and Biostatistics, New York, NY, 10021,
|
|
Keywords:
|
order statistic ; power ; optimal design
|
|
Abstract:
|
Gene-disease association studies based on case-control designs are used to identify candidate polymorphisms conferring disease risk. Here we propose a two-stage approach to identify disease susceptibility markers. In the first stage all markers are evaluated on a fraction of the available subjects. The most promising markers are further evaluated in Stage 2 on the remaining individuals. Using simulations, we show that this approach provides a substantial reduction in the total number of marker evaluations for a minimal loss of power in comparison with a one-stage approach (where all the candidate markers are evaluated on all the individuals). As a general guideline, the simulations indicate that evaluating all the markers on 50% of the individuals in Stage 1 and evaluating the most promising 10% of the markers on the remaining individuals in Stage 2 provides near optimal power while resulting in a 45% decrease in the total number of marker evaluations.
|
