Convergence properties of sequential Bayesian D-optimal designs

被引:13
|
作者
Roy, Anindya [1 ]
Ghosal, Subhashis [2 ]
Rosenberger, William F. [3 ]
机构
[1] Univ Maryland Baltimore Cty, Dept Math & Stat, Baltimore, MD 21250 USA
[2] N Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA
[3] George Mason Univ, Dept Stat, Fairfax, VA 22030 USA
基金
美国国家科学基金会;
关键词
Adaptive designs; Asymptotic normality; Discrete optimal design; Dose-response; Posterior convergence; CONTINUAL REASSESSMENT METHOD; I CLINICAL-TRIALS; DOSE-ESCALATION; INFERENCE; CONSISTENCY;
D O I
10.1016/j.jspi.2008.04.025
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We establish convergence properties of sequential Bayesian optimal designs. In particular, for sequential D-optimality under a general nonlinear location-scale model for binary experiments, we establish posterior consistency, consistency of the design measure, and the asymptotic normality of posterior following the design. We illustrate our results in the context of a particular application in the design of phase I clinical trials, namely a sequential design of Haines et al. [2003. Bayesian optimal designs for phase I clinical trials. Biornetrics 59, 591-600] that incorporates an ethical constraint on overdosing. (C) 2008 Elsevier B.V. All rights reserved.
引用
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页码:425 / 440
页数:16
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