Adaptive Adjustment of the Randomization Ratio Using Historical Control Data

• Activity Number: 408
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Brian Hobbs*+ and Bradley P. Carlin and Daniel J. Sargent
• Companies: The University of Texas M.D. Anderson Cancer Center and University of Minnesota and Mayo Clinic
• Keywords: adaptive designs ; Bayesian analysis ; clinical trials ; historical controls

Abstract

Prospective trial design often occurs in the presence of 'acceptable' (Pocock, 1976) historical control data. Typically this data is used exclusively in retrospective analysis. We propose an adaptive trial design that implements an adaptive randomization procedure for allocating patients aimed at balancing total information (concurrent and historical) among the study arms. This is accomplished by assigning more patients to receive the novel therapy in the absence of strong evidence for heterogeneity among the concurrent and historical controls. Allocation probabilities adapt as a function of the effective historical sample size (EHSS) defined in the context of a piecewise exponential model for evaluating time to event data. A Bayesian hierarchical model is used to assess historical and concurrent heterogeneity at interim analyses and to borrow strength from the historical data in the final analysis. Using the proposed hierarchical model to borrow strength from the historical data, after balancing total information with the adaptive randomization procedure, provides preposterior admissible estimators of the novel treatment effect with desirable bias-variance trade-offs.