Bayesian enrichment strategies for randomized discontinuation trials

Biometrics. 2012 Mar;68(1):203-11. doi: 10.1111/j.1541-0420.2011.01623.x. Epub 2011 Jun 29.


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Bayes Theorem*
  • Biometry / methods*
  • Computer Simulation
  • Data Interpretation, Statistical*
  • Humans
  • Models, Statistical*
  • Neoplasms / epidemiology*
  • Randomized Controlled Trials as Topic / methods*
  • Randomized Controlled Trials as Topic / statistics & numerical data*