The scale transformed power prior for use with historical data from a different outcome model

TL;DR

This paper introduces the scale transformed power prior (straPP), a novel Bayesian method that effectively incorporates historical data with different data types into current analyses, improving inference in clinical trials.

Contribution

The paper develops the straPP, a new power prior that uses Fisher information-based scale transformation to handle different data types between historical and current data.

Findings

straPP outperforms traditional power priors in simulations

Improves accuracy when combining binary and continuous data

Demonstrates practical utility with clinical trial data

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 new 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 undertaken to study a novel transitional care model for stroke survivors is used to illustrate the methodology.