Elastic Priors to Dynamically Borrow Information From Historical Data in Clinical Trials

Authors

Liyun Jiang
Lei Nie
Ying Yuan

Publication Date

3-1-2023

Journal

Biometrics

Abstract

Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is to effectively borrow information from historical data while maintaining a reasonable type I error and minimal bias. We propose the elastic prior approach to address this challenge. Unlike existing approaches, this approach proactively controls the behavior of information borrowing and type I errors by incorporating a well-known concept of clinically significant difference through an elastic function, defined as a monotonic function of a congruence measure between historical data and trial data. The elastic function is constructed to satisfy a set of prespecified criteria such that the resulting prior will strongly borrow information when historical and trial data are congruent, but refrain from information borrowing when historical and trial data are incongruent. The elastic prior approach has a desirable property of being information borrowing consistent, that is, asymptotically controls type I error at the nominal value, no matter that historical data are congruent or not to the trial data. Our simulation study that evaluates the finite sample characteristic confirms that, compared to existing methods, the elastic prior has better type I error control and yields competitive or higher power. The proposed approach is applicable to binary, continuous, and survival endpoints.

Keywords

Research Design, Bayes Theorem, Computer Simulation, Sample Size, Bias, Models, Statistical, adaptive design, dynamic information borrowing, elastic MAP prior, elastic prior, historical data, real-world data

DOI

10.1111/biom.13551

PMID

34437714

PMCID

PMC11840877

PubMedCentral® Posted Date

2-20-2025

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes