title: A decision-theoretic phase I-II design for ordinal outcomes in two cycles.
abstract: This paper is motivated by a phase I-II clinical trial of a targeted agent for advanced solid tumors. We study a stylized version of this trial with the goal to determine optimal actions in each of two cycles of therapy. A design is presented that generalizes the decision-theoretic two-cycle design of Lee and others (2015. Bayesian dose-finding in two treatment cycles based on the joint utility of efficacy and toxicity. Journal of the American Statistical Association, to appear) to accommodate ordinal outcomes. Backward induction is used to jointly optimize the actions taken for each patient in each of the two cycles, with the second action accounting for the patient's cycle 1 dose and outcomes. A simulation study shows that simpler designs obtained by dichotomizing the ordinal outcomes either perform very similarly to the proposed design, or have much worse performance in some scenarios. We also compare the proposed design with the simpler approaches of optimizing the doses in each cycle separately, or ignoring the distinction between cycles 1 and 2.
authors.affiliation: Department of Applied Mathematics and Statistics, Baskin School of Engineering, University of California, 1156 High Street, Mail Stop SOE2, Santa Cruz, CA 95064, USA juheelee@soe.ucsc.edu.
authors.forename: Juhee
authors.initials: J
authors.lastname: Lee
authors.affiliation: Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
authors.forename: Peter F
authors.initials: PF
authors.lastname: Thall
authors.affiliation: Program of Computational Genomics & Medicine, NorthShore University Health System, Evanston, IL, USA and Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Ji
authors.affiliation: Department of Mathematics, University of Texas, Austin, TX, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Müller
mesh_terms: D017321:Clinical Trials, Phase I as Topic; D017322:Clinical Trials, Phase II as Topic; D003662:Decision Theory; D006801:Humans; D012107:Research Design
keywords: Adaptive design; Bayesian design; Decision theory; Dynamic treatment regime; Latent probit model; Ordinal outcomes; Phase I–II clinical trial