O'Quigley John, Paoletti Xavier, Maccario Jean
Department of Mathematics, University of California, San Diego, CA 92093, USA.
Biostatistics. 2002 Mar;3(1):51-6. doi: 10.1093/biostatistics/3.1.51.
We describe a non-parametric optimal design as a theoretical gold standard for dose finding studies. Its purpose is analogous to the Cramer-Rao bound for unbiased estimators, i.e. it provides a bound beyond which improvements are not generally possible. The bound applies to the class of non-parametric designs where the data are not assumed to be generated by any known parametric model. Whenever parametric assumptions really hold it may be possible to do better than the optimal non-parametric design. The goal is to be able to compare any potential dose finding scheme with the optimal non-parametric benchmark. This paper makes precise what is meant by optimal in this context and also why the procedure is described as non-parametric.
我们将非参数最优设计描述为剂量探索研究的理论金标准。其目的类似于无偏估计量的克拉美 - 罗界，即它提供了一个界限，一般来说超出这个界限就不太可能有改进。该界限适用于不假定数据由任何已知参数模型生成的非参数设计类别。只要参数假设确实成立，就有可能比最优非参数设计做得更好。目标是能够将任何潜在的剂量探索方案与最优非参数基准进行比较。本文明确了在此背景下最优的含义，以及为何该程序被描述为非参数的。
