title: A Bayesian predictive two-stage design for phase II clinical trials.
abstract: In this paper, we propose a Bayesian two-stage design for phase II clinical trials, which represents a predictive version of the single threshold design (STD) recently introduced by Tan and Machin. The STD two-stage sample sizes are determined specifying a minimum threshold for the posterior probability that the true response rate exceeds a pre-specified target value and assuming that the observed response rate is slightly higher than the target. Unlike the STD, we do not refer to a fixed experimental outcome, but take into account the uncertainty about future data. In both stages, the design aims to control the probability of getting a large posterior probability that the true response rate exceeds the target value. Such a probability is expressed in terms of prior predictive distributions of the data. The performance of the design is based on the distinction between analysis and design priors, recently introduced in the literature. The properties of the method are studied when all the design parameters vary.
authors.affiliation: Department of Statistics, Probability and Applied Statistics, University of Rome La Sapienza, Rome, Italy. valeria.sambucini@uniroma1.it
authors.forename: Valeria
authors.initials: V
authors.lastname: Sambucini
mesh_terms: D001499:Bayes Theorem; D017322:Clinical Trials, Phase II as Topic / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D009303:Nasopharyngeal Neoplasms / Q000188:drug therapy; D011237:Predictive Value of Tests; D011336:Probability; D012107:Research Design; D018401:Sample Size; D035501:Uncertainty