title: Bayesian decision procedures for binary and continuous bivariate dose-escalation studies.
abstract: In this paper, Bayesian decision procedures are developed for dose-escalation studies based on binary measures of undesirable events and continuous measures of therapeutic benefit. The methods generalize earlier approaches where undesirable events and therapeutic benefit are both binary. A logistic regression model is used to model the binary responses, while a linear regression model is used to model the continuous responses. Prior distributions for the unknown model parameters are suggested. A gain function is discussed and an optional safety constraint is included.
authors.affiliation: Medical and Pharmaceutical Statistics Research Unit, The University of Reading, UK. y.zhou@reading.ac.uk
authors.forename: Yinghui
authors.initials: Y
authors.lastname: Zhou
authors.forename: John
authors.initials: J
authors.lastname: Whitehead
authors.forename: Elisa
authors.initials: E
authors.lastname: Bonvini
authors.forename: John W
authors.initials: JW
authors.lastname: Stevens
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D004305:Dose-Response Relationship, Drug; D006801:Humans; D020714:Maximum Tolerated Dose; D012044:Regression Analysis