Hierarchical commensurate and power prior models for adaptive incorporation of historical information in clinical trials.

ABSTRACT: Bayesian clinical trial designs offer the possibility of a substantially reduced sample size, increased statistical power, and reductions in cost and ethical hazard. However when prior and current information conflict, Bayesian methods can lead to higher than expected type I error, as well as the possibility of a costlier and lengthier trial. This motivates an investigation of the feasibility of hierarchical Bayesian methods for incorporating historical data that are adaptively robust to prior information that reveals itself to be inconsistent with the accumulating experimental data. In this article, we present several models that allow for the commensurability of the information in the historical and current data to determine how much historical information is used. A primary tool is elab

SUBMITTER: Hobbs BP 

PROVIDER: S-EPMC3134568 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature