title: A curve-free Bayesian decision-theoretic design for two-agent Phase I trials.
abstract: Although Bayesian statistical methods are gaining attention in the medical community, as they provide a natural framework for incorporating prior information, the complexity of these methods limited their adoptions in clinical trials. This article proposes a Bayesian design for two-agent Phase I trials that is relatively easy for clinicians to understand and implement, yet performs comparably to more complex designs, so that it is more likely to be adopted in actual trials. In order to reduce model complexity and computational burden, we choose a working model with conjugate priors so that the posterior distributions have analytical expressions. Furthermore, we provide a simple strategy to facilitate the specification of priors based on the toxicity information accrued from single-agent Phase I trials. The proposed method should be useful in terms of the ease of implementation and the savings in sample size without sacrificing performance. Moreover, the conservativeness of the dose-finding algorithm renders it a relatively safe method.
authors.affiliation: a Department of Mathematics and Statistics , San José State University , San José , California , USA.
authors.forename: Bee L
authors.initials: BL
authors.lastname: Lee
authors.affiliation: b Department of Statistics and Biostatistics , California State University at East Bay , Hayward , California , USA.
authors.forename: Shenghua K
authors.initials: SK
authors.lastname: Fan
authors.affiliation: c Department of Health Research and Policy , Stanford University , Stanford , California , USA.|d The Cooperative Studies Program Coordinating Center , VA Palo Alto Health Care System , Palo Alto , California , USA.
authors.forename: Ying
authors.initials: Y
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D017321:Clinical Trials, Phase I as Topic; D004305:Dose-Response Relationship, Drug; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D018401:Sample Size
keywords: Dose allocation; Phase I trial; maximum tolerated dose; stopping rules; two-agent trials; utility function