A Randomized Two-Stage Design for Phase II Clinical Trials Based on a Bayesian Predictive Approach

Stat Med. 2015 Mar 15;34(6):1059-78. doi: 10.1002/sim.6396. Epub 2014 Dec 29.

Abstract

The rate of failure in phase III oncology trials is surprisingly high, partly owing to inadequate phase II studies. Recently, the use of randomized designs in phase II is being increasingly recommended, to avoid the limits of studies that use a historical control. We propose a two-arm two-stage design based on a Bayesian predictive approach. The idea is to ensure a large probability, expressed in terms of the prior predictive probability of the data, of obtaining a substantial posterior evidence in favour of the experimental treatment, under the assumption that it is actually more effective than the standard agent. This design is a randomized version of the two-stage design that has been proposed for single-arm phase II trials by Sambucini. We examine the main features of our novel design as all the parameters involved vary and compare our approach with Jung's minimax and optimal designs. An illustrative example is also provided online as a supplementary material to this article.

Keywords: Bayesian predictive approach; analysis and design priors; phase II clinical trials; randomized trials; two-stage designs.

MeSH terms

  • Bayes Theorem*
  • Clinical Trials, Phase II as Topic / methods*
  • Data Interpretation, Statistical
  • Humans
  • Models, Statistical
  • Neoplasms
  • Predictive Value of Tests
  • Probability
  • Randomized Controlled Trials as Topic*
  • Sample Size