title: TEAMS: Toxicity- and Efficacy-based Dose Insertion Design with Adaptive Model Selection for Phase I/II Dose-Escalation Trials in Oncology.
abstract: In many oncology clinical trials it is necessary to insert new candidate doses when the prespecified doses are poorly elicited. Formal statistical designs with dose insertion are lacking. We propose a dose insertion design for phase I/II clinical trials in oncology based on both efficacy and toxicity outcomes. We also implement Bayesian model selection during the course of the trial so that better models can be adaptively chosen to achieve more accurate inference. The new design, TEAMS, achieves great operating characteristics in extensive simulation studies due to its ability to adaptively insert new doses as well as perform model selection. As a result, appropriate doses are inserted when necessary and desirable doses are selected with higher probabilities at the end of the trial.
authors.affiliation: Department of Biostatistics, Fudan University, Shanghai, China.
authors.forename: Wentian
authors.initials: W
authors.lastname: Guo
authors.affiliation: Department of Statistics, Rice University, Houston, USA.
authors.forename: Yang
authors.initials: Y
authors.lastname: Ni
authors.affiliation: Program of Computational Genomics & Medcine, NorthShore University HealthSystem, Evanston, IL, USA, Department of Public Health Sciences, The University of Chicago, Chicago, USA, TEL: 224.364.7312, koaeraser@gmail.com.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Ji
keywords: Adaptive model selection; Bayesian inference; Dose insertion; Hierarchical models; Markov chain Monte Carlo simulation; Phase I/II clinical trial