Bayesian decision procedures for dose-escalation based on evidence of undesirable events and therapeutic benefit.Whitehead, J., Zhou, Y., Stevens, J., Blakey, G., Price, J. and Leadbetter, J. (2006) Bayesian decision procedures for dose-escalation based on evidence of undesirable events and therapeutic benefit. Statistics in Medicine, 25 (1). pp. 37-53. ISSN 0277-6715 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1002/sim.2201 Abstract/SummaryIn this paper, Bayesian decision procedures are developed for dose-escalation studies based on bivariate observations of undesirable events and signs of therapeutic benefit. The methods generalize earlier approaches taking into account only the undesirable outcomes. Logistic regression models are used to model the two responses, which are both assumed to take a binary form. A prior distribution for the unknown model parameters is suggested and an optional safety constraint can be included. Gain functions to be maximized are formulated in terms of accurate estimation of the limits of a therapeutic window or optimal treatment of the next cohort of subjects, although the approach could be applied to achieve any of a wide variety of objectives. The designs introduced are illustrated through simulation and retrospective implementation to a completed dose-escalation study. Copyright © 2006 John Wiley & Sons, Ltd.
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