|
Activity Number:
|
457
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Wednesday, August 1, 2007 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Biometrics Section
|
| Abstract - #309085 |
|
Title:
|
Toward a Benchmark for Sequential Phase I Cancer Trial Designs
|
|
Author(s):
|
Jay Bartroff*+ and Tze Leung Lai
|
|
Companies:
|
University of Southern California and Stanford University
|
|
Address:
|
Department of Mathematics, Los Angeles, CA, 90089-2532,
|
|
Keywords:
|
phase I ; sequential design ; dynamic programming ; Monte Carlo ; dose finding
|
|
Abstract:
|
Phase I cancer trials have two competing aims: treating advanced stage patients at a dose close to the maximum tolerated dose (MTD) for their therapy, and active experimentation to obtain an accurate estimate of the MTD for use in a subsequent phase II trial. Designs have been proposed to balance these aims, like "continual reassessment" by O`Quigley et al. (1990) and "escalation with overdose control" by Babb et al. (1998). As a benchmark for such designs we consider an optimal sequential design, the computational complexity of which would limit its practical use for even a two parameter dose-response model, but which can be accurately approximated using recent advances in approximate dynamic programming and Monte Carlo simulation. We compare current designs to this near-optimum and discuss what intuition it can provide into the optimal balance between therapy and experimentation.
|
