Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials
Article first published online: 1 MAR 2011
© 2011, The International Biometric Society
Volume 67, Issue 3, pages 1047–1056, September 2011
How to Cite
Hobbs, B. P., Carlin, B. P., Mandrekar, S. J. and Sargent, D. J. (2011), Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials. Biometrics, 67: 1047–1056. doi: 10.1111/j.1541-0420.2011.01564.x
- Issue published online: 14 SEP 2011
- Article first published online: 1 MAR 2011
- Received November 2009. Revised October 2010. Accepted October 2010.
- Adaptive designs;
- Clinical trials;
- Colorectal cancer;
- Power priors
Summary 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 elaborating the traditional power prior approach based upon a measure of commensurability for Gaussian data. We compare the frequentist performance of several methods using simulations, and close with an example of a colon cancer trial that illustrates a linear models extension of our adaptive borrowing approach. Our proposed methods produce more precise estimates of the model parameters, in particular, conferring statistical significance to the observed reduction in tumor size for the experimental regimen as compared to the control regimen.