title: Bayesian decision procedures for dose-escalation based on evidence of undesirable events and therapeutic benefit.
abstract: 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.
authors.affiliation: Medical and Pharmaceutical Statistics Research Unit, The University of Reading, UK. j.r.whitehead@reading.ac.uk
authors.forename: John
authors.initials: J
authors.lastname: Whitehead
authors.forename: Yinghui
authors.initials: Y
authors.lastname: Zhou
authors.forename: John
authors.initials: J
authors.lastname: Stevens
authors.forename: Graham
authors.initials: G
authors.lastname: Blakey
authors.forename: Jim
authors.initials: J
authors.lastname: Price
authors.forename: Joanna
authors.initials: J
authors.lastname: Leadbetter
mesh_terms: D000925:Anticoagulants / Q000494:pharmacology / Q000627:therapeutic use; D001499:Bayes Theorem; D017321:Clinical Trials, Phase I as Topic / Q000379:methods*; D003657:Decision Making; D004305:Dose-Response Relationship, Drug; D065427:Factor Xa Inhibitors; D006801:Humans; D016015:Logistic Models; D020714:Maximum Tolerated Dose; D015233:Models, Statistical; D013923:Thromboembolism / Q000188:drug therapy
chemical_list: D000925:Anticoagulants; D065427:Factor Xa Inhibitors