Up-and-Down Designs for Selecting the Dose with Maximum Success Probability

• Published in: Sequential analysis Vol. 27; no. 1; pp. 78 - 96
• Main Authors: Kpamegan, E. E., Flournoy, N.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 30.01.2008
• Subjects: Constant gain stochastic algorithm; Dose-finding; Efficacy; Markov chains; Optimal dose; Phase I/II clinical trial; Random walk designs; Stationary treatment distribution; Stochastic approximation; Toxicity; Unimodality
• ISSN: 0747-4946; 1532-4176
• DOI: 10.1080/07474940701802034

Assume that the probability of success is unimodal as a function of dose, such as may be the case when too much of a drug is toxic and too little is ineffective. We characterize a class of up-and-down designs, that is, treatment allocation methodologies, for identifying the dose that maximizes the patients' success probability. These designs are constructed to use accruing information to limit the number of patients that are exposed to doses with high probabilities of failure. This treatment allocation procedure is motivated by Kiefer-Wolfowitz's stochastic approximation procedure. However, we take the response to be binary and the possible treatment space to be a lattice. The procedure is shown to allocate treatments to pairs of subjects in a way that causes the treatment distribution to center around the treatment with maximum success probability. The procedure defines a nonhomogeneous random walk, so well-known theory is used to explicitly characterize the treatment distribution. As an estimator of the best dose, the mode of the empirical treatment distribution is shown to converge faster than does the last dose allocated, which is used as an estimator of the optimal dose in stochastic approximation procedures.