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Bayesian decision procedures for dose‐escalation based on evidence of undesirable events and therapeutic benefit
Author(s) -
Whitehead John,
Zhou Yinghui,
Stevens John,
Blakey Graham,
Price Jim,
Leadbetter Joanna
Publication year - 2005
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2201
Subject(s) - bayesian probability , bivariate analysis , logistic regression , computer science , constraint (computer aided design) , econometrics , bayesian inference , statistics , mathematics , machine learning , artificial intelligence , geometry
In 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.