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Bayesian Enrichment Strategies for Randomized Discontinuation Trials
Author(s) -
Trippa Lorenzo,
Rosner Gary L.,
Müller Peter
Publication year - 2012
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2011.01623.x
Subject(s) - discontinuation , randomization , bayesian probability , duration (music) , computer science , maximum tolerated dose , stage (stratigraphy) , randomized controlled trial , medicine , statistics , mathematics , clinical trial , artificial intelligence , art , paleontology , literature , biology
Summary 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.