Bayesian decision procedures for dose-escalation based on evidence of undesirable events and therapeutic benefit

• Published in: Statistics in medicine Vol. 25; no. 1; pp. 37 - 53
• Main Authors: Whitehead, John, Zhou, Yinghui, Stevens, John, Blakey, Graham, Price, Jim, Leadbetter, Joanna
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 15.01.2006; Wiley Subscription Services, Inc
• Subjects: Anticoagulants - pharmacology; Anticoagulants - therapeutic use; Bayes Theorem; Bayesian analysis; Bayesian decision procedure; Clinical Trials, Phase I as Topic - methods; continual reassessment method; Decision Making; dose-escalation; Dose-Response Relationship, Drug; Factor Xa Inhibitors; Humans; Logistic Models; logistic regression; Maximum Tolerated Dose; Models, Statistical; phase I clinical trial; Regression analysis; Therapy; Thromboembolism - drug therapy
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.2201

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.