The Scale-Transformed Power Prior for Use with Historical Data from a Different Outcome Model

• Activity Number: 4 - Recent Advancements in Prior Elicitation and Computational Tools for Bayesian Design and Analysis
• Type: Invited
• Date/Time: Sunday, August 7, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Joseph G Ibrahim* and Brady G Nifong and Matthew A. Psioda
• Companies: University of North Carolina and University of North Carolina and University of North Carolina at Chapel Hill
• Keywords: power prior; historical data; information borrowing; heterogenous endpoints; Bayesian methods

Abstract

We develop the scale transformed power prior for settings where historical and current data involve different data types, such as binary and continuous data, respectively. This situation arises often in clinical trials, for example, when historical data involve binary responses and the current data involve time-to-event or some other type of continuous or discrete outcome. The power prior proposed by Ibrahim and Chen (2000) does not address the issue of different data types. Herein, we develop a current type of power prior, which we call the scale transformed power prior (straPP). The straPP is constructed by transforming the power prior for the historical data by rescaling the parameter using a function of the Fisher information matrices for the historical and current data models, thereby shifting the scale of the parameter vector from that of the historical to that of the current data. Examples are presented to motivate the need for a scale transformation and simulation studies are presented to illustrate the performance advantages of the straPP over the power prior and other informative and non-informative priors. A real dataset from a clinical trial is presented.