title: Bayesian enrichment strategies for randomized discontinuation trials.
abstract: We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identifies a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identified subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of follow-up after randomization. We define a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.
authors.affiliation: Harvard School of Public Health and Department of Biostatistics, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. lorenzo.trippa@jimmi.harvard.com
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Trippa
authors.forename: Gary L
authors.initials: GL
authors.lastname: Rosner
authors.forename: Peter
authors.initials: P
authors.lastname: Müller
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000453:epidemiology*; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data*