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Biostatistics 3:51-56 (2002)
© 2002 Oxford University Press

Non-parametric optimal design in dose finding studies

John O'quigley, Xavier Paoletti and Jean Maccario

Department of Mathematics, University of California, San Diego, CA 92093, USA. joquigle{at}ucsd.edu
Unité 472 de l'INSERM, 16 Avenue P.V. Couturier, 94807 Villejuif, France

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.

Keywords: Clinical trials; Dose finding; Efficiency; Non-parametric; Optimality; Phase I


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