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  • Poster presentation
  • Open Access

Some small sample properties of the EWOC design in dose escalation trials

  • 1
Trials201516 (Suppl 2) :P221

  • Published:


  • Public Health
  • Logistic Model
  • Prior Distribution
  • Dose Escalation
  • Sample Property

Model-based approaches to dose escalation trials such as CRM, EWOC and their variants are becoming increasingly popular in Phase I oncology trials. Although the literature includes a number of reasons why the practitioner may want to consider using a model-based design over a more traditional 3+3 design, there are still a number of reasons why they are not universally adopted. Theoretical reasons have often been provided for their superiority over 3+3 designs and these have generally relied on large sample theory, whereas Phase I trial sizes are usually considerably lower. Furthermore, when information external to the planned trial is available and may be used to construct informative prior distributions, the small sample properties of model-based designs are less well understood.

By considering a planned trial for which only a discrete set of doses are available, we use simulation to show some small sample properties of the EWOC design (Babb et al, 1998). When the model recommends a dose between the available doses, we evaluate the strategy of always choosing the dose below that recommended by the model versus the closest available dose (even if that is higher). We also characterise two different parameterisations of the Bayesian 2-parameter logistic model that underpins the EWOC design and make some recommendations that could increase the acceptability of the EWOC design to those more comfortable with implementing a 3+3 design.

Authors’ Affiliations

Roche Products Ltd, Welwyn, UK


© Kinnersley 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.


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