Open Access
May 2010 Approximate Dynamic Programming and Its Applications to the Design of Phase I Cancer Trials
Jay Bartroff, Tze Leung Lai
Statist. Sci. 25(2): 245-257 (May 2010). DOI: 10.1214/10-STS317


Optimal design of a Phase I cancer trial can be formulated as a stochastic optimization problem. By making use of recent advances in approximate dynamic programming to tackle the problem, we develop an approximation of the Bayesian optimal design. The resulting design is a convex combination of a “treatment” design, such as Babb et al.’s (1998) escalation with overdose control, and a “learning” design, such as Haines et al.’s (2003) c-optimal design, thus directly addressing the treatment versus experimentation dilemma inherent in Phase I trials and providing a simple and intuitive design for clinical use. Computational details are given and the proposed design is compared to existing designs in a simulation study. The design can also be readily modified to include a first stage that cautiously escalates doses similarly to traditional nonparametric step-up/down schemes, while validating the Bayesian parametric model for the efficient model-based design in the second stage.


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Jay Bartroff. Tze Leung Lai. "Approximate Dynamic Programming and Its Applications to the Design of Phase I Cancer Trials." Statist. Sci. 25 (2) 245 - 257, May 2010.


Published: May 2010
First available in Project Euclid: 19 November 2010

zbMATH: 1328.62581
MathSciNet: MR2789993
Digital Object Identifier: 10.1214/10-STS317

Keywords: dynamic programming , maximum tolerated dose , Monte Carlo , rollout , stochastic optimization

Rights: Copyright © 2010 Institute of Mathematical Statistics

Vol.25 • No. 2 • May 2010
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