Literature DB >> 16220506

An evaluation of Bayesian designs for dose-escalation studies in healthy volunteers.

John Whitehead1, Yinghui Zhou, Adrian Mander, Sally Ritchie, Antony Sabin, Andrew Wright.   

Abstract

In this paper, Bayesian decision procedures previously proposed for dose-escalation studies in healthy volunteers are reviewed and evaluated. Modifications are made to the expression of the prior distribution in order to make the procedure simpler to implement and a more relevant criterion for optimality is introduced. The results of an extensive simulation exercise to establish the properties of the procedure and to aid choice between designs are summarized, and the way in which readers can use simulation to choose a design for their own trials is described. The influence of the value of the within-subject correlation on the procedure is investigated and the use of a simple prior to reflect uncertainty about the correlation is explored.

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Year:  2006        PMID: 16220506     DOI: 10.1002/sim.2213

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  3 in total

1.  Dose finding for continuous and ordinal outcomes with a monotone objective function: a unified approach.

Authors:  Anastasia Ivanova; Se Hee Kim
Journal:  Biometrics       Date:  2008-05-13       Impact factor: 2.571

2.  Adaptive Phase I clinical trial design using Markov models for conditional probability of toxicity.

Authors:  Laura L Fernandes; Jeremy M G Taylor; Susan Murray
Journal:  J Biopharm Stat       Date:  2015-06-22       Impact factor: 1.051

3.  Dose-escalation strategies which use subgroup information.

Authors:  Amy Cotterill; Thomas Jaki
Journal:  Pharm Stat       Date:  2018-06-13       Impact factor: 1.894

  3 in total

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