Buzoianu Manuela, Kadane Joseph B
Department of Biostatistics, MedImmune, Gaithersburg, Maryland 20878, USA.
Biometrics. 2009 Sep;65(3):953-61. doi: 10.1111/j.1541-0420.2008.01156.x. Epub 2008 Nov 14.
Bayesian experimental design for a clinical trial involves specifying a utility function that models the purpose of the trial, in this case the selection of patients for a diagnostic test. The best sample of patients is selected by maximizing expected utility. This optimization task poses difficulties due to a high-dimensional discrete design space and, also, to an expected utility formula of high complexity. A simulation-based optimal design method is feasible in this case. In addition, two deterministic algorithms that perform a systematic search over the design space are developed to address the computational issues.
用于临床试验的贝叶斯实验设计涉及指定一个效用函数，该函数对试验目的进行建模，在这种情况下是为诊断测试选择患者。通过最大化期望效用选择最佳患者样本。由于高维离散设计空间以及复杂度高的期望效用公式，这个优化任务带来了困难。在这种情况下，基于模拟的最优设计方法是可行的。此外，还开发了两种在设计空间上进行系统搜索的确定性算法来解决计算问题。
