This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 552
Type: Invited
Date/Time: Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #305949
Title: Hierarchical Gaussian Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials
Author(s): Bradley P. Carlin*+ and Brian P. Hobbs and Daniel Sargent and Sumithra Mandrekar
Companies: University of Minnesota and University of Minnesota and Mayo Clinic and Mayo Clinic
Address: MMC 303 Division of Biostatistics, Minneapolis, MN, 55455,
Keywords: adaptive designs ; Bayesian methods ; colon cancer ; clinical trials ; power priors ; borrowing strength
Abstract:

Bayesian clinical trial designs offer the possibility of a substantially reduced sample size, increased statistical power, and reductions in cost and ethical hazard. In this paper, we present novel adaptive modifications to the traditional power prior approach for Gaussian data that measure the commensurability of the information in the historical and current data to determine how much historical information is used. We compare the frequentist performance of the various methods using simulation, and close with an example from the field of colon cancer that illustrates a linear models extension of our adaptive borrowing approach. Our design produces 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.


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