An optimization algorithm for designing phase I cancer clinical trials

Received 9 February 2007; accepted 19 June 2007.

Abstract

Numerous phase I dose finding clinical trials are conducted everyday to find the “maximum tolerated dose” (MTD) of a cancer treatment. Although various Bayesian designs for Phase I clinical trials have been proposed in the literature, the traditional 3+3 design is still widely used because of its algorithm-based simplicity in logistics for the clinical investigators to carry out in comparison with model-based Bayesian methods. In this paper, we propose an optimization algorithm for designing phase I cancer clinical trials. This algorithm does not need assumptions on the true dose-response relationship but can readily incorporate available prior information about the true response probabilities. It searches for an approximately optimal design within a design space, in which the 3+3 design is included as a special case. Simulation studies show that the design recommended by this algorithm significantly outperforms the 3+3 design.

Keywords: Clinical trial, MTD, Dose escalation, Phase I, Local search

a Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, USA

b Department of Systems Engineering and Operations Research, George Mason University, Fairfax, Virginia, USA

c Department of Applied Mathematics and Statistics, SUNY Stony Brook, Stony Brook, New York, USA

Corresponding author. Tel.: +1 631 632 4408; fax: +1 631 632 8490.

PII: S1551-7144(07)00086-9

doi:10.1016/j.cct.2007.06.003

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