Literature DB >> 18663758

Dose finding - a challenge in statistics.

Frank Bretz1, Jason Hsu, José Pinheiro, Yi Liu.   

Abstract

A good understanding and characterization of the dose response relationship of any new compound is an important and ubiquitous problem in many areas of scientific investigation. This is especially true in the context of pharmaceutical drug development, where it is mandatory to launch safe drugs which demonstrate a clinically relevant effect. Selecting a dose too high may result in unacceptable safety problems, while selecting a dose too low may lead to ineffective drugs. Dose finding studies thus play a key role in any drug development program and are often the gate-keeper for large confirmatory studies. In this overview paper we focus on definitive and confirmatory dose finding studies in Phase II or III, reviewing relevant statistical design and analysis methods. In particular, we describe multiple comparison procedures, modeling approaches, and hybrid methods combining the advantages of both. An outlook to adaptive dose finding methods is also given. We use a real data example to illustrate the methods, together with a brief overview of relevant software. (c) 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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Year:  2008        PMID: 18663758     DOI: 10.1002/bimj.200810438

Source DB:  PubMed          Journal:  Biom J        ISSN: 0323-3847            Impact factor:   2.207


  21 in total

1.  An example of optimal phase II design for exposure response modelling.

Authors:  Alan Maloney; Marloes Schaddelee; Jan Freijer; Walter Krauwinkel; Marcel van Gelderen; Philippe Jacqmin; Ulrika S H Simonsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2010-09-25       Impact factor: 2.745

2.  Two-Stage Experimental Design for Dose-Response Modeling in Toxicology Studies.

Authors:  Kai Wang; Feng Yang; Dale W Porter; Nianqiang Wu
Journal:  ACS Sustain Chem Eng       Date:  2013-06-27       Impact factor: 8.198

3.  Improved precision of exposure-response relationships by optimal dose-selection. Examples from studies of receptor occupancy using PET and dose finding for neuropathic pain treatment.

Authors:  Matts Kågedal; Mats O Karlsson; Andrew C Hooker
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-03-20       Impact factor: 2.745

4.  Implementing Optimal Designs for Dose-Response Studies Through Adaptive Randomization for a Small Population Group.

Authors:  Yevgen Ryeznik; Oleksandr Sverdlov; Andrew C Hooker
Journal:  AAPS J       Date:  2018-07-19       Impact factor: 4.009

5.  Dose finding by concentration-response versus dose-response: a simulation-based comparison.

Authors:  Alienor Berges; Chao Chen
Journal:  Eur J Clin Pharmacol       Date:  2013-02-06       Impact factor: 2.953

6.  Simultaneous confidence bands for comparisons to placebo, with application to detecting the minimum effective dose.

Authors:  Julia N Soulakova; Allan R Sampson; Gang Jia; Leon J Gleser
Journal:  J Biopharm Stat       Date:  2012       Impact factor: 1.051

7.  Practical considerations for optimal designs in clinical dose finding studies.

Authors:  Frank Bretz; Holger Dette; Jose C Pinheiro
Journal:  Stat Med       Date:  2010-03-30       Impact factor: 2.373

8.  Expected loss functions as additional measures to assess performance of multiple testing procedures for combination drug dose finding.

Authors:  Julia N Soulakova; Allan R Sampson
Journal:  Pharm Stat       Date:  2012-03-06       Impact factor: 1.894

9.  Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation.

Authors:  S F Marshall; R Burghaus; V Cosson; S Y A Cheung; M Chenel; O DellaPasqua; N Frey; B Hamrén; L Harnisch; F Ivanow; T Kerbusch; J Lippert; P A Milligan; S Rohou; A Staab; J L Steimer; C Tornøe; S A G Visser
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2016-03-14

10.  Designing dose finding studies with an active control for exponential families.

Authors:  Holger Dette; Katrin Kettelhake; Frank Bretz
Journal:  Biometrika       Date:  2015-11-04       Impact factor: 2.445

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