Statistics – Methodology
Scientific paper
2010-11-30
Statistical Science 2010, Vol. 25, No. 2, 245-257
Statistics
Methodology
Published in at http://dx.doi.org/10.1214/10-STS317 the Statistical Science (http://www.imstat.org/sts/) by the Institute of M
Scientific paper
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.
Bartroff Jay
Lai Tze Leung
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