Literature DB >> 12933623

Non-parametric optimal design in dose finding studies.

John O'Quigley1, Xavier Paoletti, Jean Maccario.   

Abstract

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.

Year:  2002        PMID: 12933623     DOI: 10.1093/biostatistics/3.1.51

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  33 in total

1.  Incorporating lower grade toxicity information into dose finding designs.

Authors:  Alexia Iasonos; Sarah Zohar; John O'Quigley
Journal:  Clin Trials       Date:  2011-08       Impact factor: 2.486

2.  Integrating the escalation and dose expansion studies into a unified Phase I clinical trial.

Authors:  Alexia Iasonos; John O'Quigley
Journal:  Contemp Clin Trials       Date:  2016-07-05       Impact factor: 2.226

3.  Phase I Designs that Allow for Uncertainty in the Attribution of Adverse Events.

Authors:  Alexia Iasonos; John O'Quigley
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2016-11-07       Impact factor: 1.864

4.  Adaptive dose-finding based on safety and feasibility in early-phase clinical trials of adoptive cell immunotherapy.

Authors:  Nolan A Wages; Camilo E Fadul
Journal:  Clin Trials       Date:  2019-12-19       Impact factor: 2.486

5.  Continual Reassessment and Related Dose-Finding Designs.

Authors:  John O'Quigley; Mark Conaway
Journal:  Stat Sci       Date:  2010       Impact factor: 2.901

6.  Stochastic approximation with virtual observations for dose-finding on discrete levels.

Authors:  Ying Kuen Cheung; Mitchell S V Elkind
Journal:  Biometrika       Date:  2009-12-07       Impact factor: 2.445

7.  Phase I design for completely or partially ordered treatment schedules.

Authors:  Nolan A Wages; John O'Quigley; Mark R Conaway
Journal:  Stat Med       Date:  2013-09-30       Impact factor: 2.373

8.  Coherence principles in interval-based dose finding.

Authors:  Nolan A Wages; Alexia Iasonos; John O'Quigley; Mark R Conaway
Journal:  Pharm Stat       Date:  2019-11-06       Impact factor: 1.894

9.  Simple benchmark for complex dose finding studies.

Authors:  Ying Kuen Cheung
Journal:  Biometrics       Date:  2014-02-25       Impact factor: 2.571

10.  Sample size formulae for the Bayesian continual reassessment method.

Authors:  Ying Kuen Cheung
Journal:  Clin Trials       Date:  2013-08-21       Impact factor: 2.486

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