Bayesian Enrichment Strategies for Randomized Discontinuation Trials

Lorenzo Trippa, Gary L. Rosner, Peter Müller

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)203-211
Number of pages9
JournalBiometrics
Volume68
Issue number1
DOIs
StatePublished - Mar 2012

Keywords

  • Clinical trials
  • Enrichment designs
  • Randomized discontinuation design
  • Tumor growth models

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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