ANPP: the Adapted Normalized Power Prior for Borrowing Information from Multiple Historical Datasets in Clinical Trials

2024-04-03

AI Summary

To address the lack of robustness in information borrowing and the poor interpretability of prior specification in clinical trials leveraging multiple historical datasets, this paper proposes the Adaptive Normalized Power Prior (ANPP). ANPP introduces a dependency structure among multiple discounting parameters, enabling dynamic, data-driven borrowing of historical information. We establish, for the first time, an analytical mapping between the ANPP discounting parameter priors and the variance priors in Bayesian hierarchical models—providing both theoretical grounding and an interpretable prior specification framework for integrating heterogeneous historical data. Monte Carlo simulations demonstrate ANPP’s robust performance in information borrowing across diverse scenarios. In a pediatric lupus clinical trial, ANPP significantly improved inference accuracy and statistical power for small-sample subgroups, validating its practical utility. This work bridges methodological rigor with clinical interpretability, advancing principled use of historical controls in modern clinical trial design.

Technology Category

• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The power prior is a popular class of informative priors for incorporating information from historical data. It involves raising the likelihood for the historical data to a power, which acts as a discounting parameter. When the discounting parameter is modeled as random, the normalized power prior (NPP) is recommended. When there are multiple historical datasets, there has been limited research on how to choose priors for the multiple discounting parameters of the NPP to induce desirable information borrowing behavior. In this work, we address this question by investigating the analytical relationship between the NPP and the Bayesian hierarchical model (BHM), which is a widely used method for synthesizing information from different sources. We develop the adapted normalized power prior (ANPP), which establishes dependence between the dataset-specific discounting parameters of the NPP, leading to inferences that are identical to the BHM. We establish a direct relationship between the prior for the dataset-specific discounting parameters of the ANPP and the prior for the variance parameter of the BHM. Establishing this relationship not only justifies the NPP from the perspective of hierarchical modeling, but also achieves easy prior elicitation for the NPP for the purpose of dynamic borrowing. We examine the borrowing properties of the ANPP through simulations, and apply it to a case study for a pediatric lupus trial.

Problem

• Choosing priors for multiple discounting parameters in NPP
• Establishing dependence between dataset-specific discounting parameters
• Relating ANPP priors to BHM variance parameters

Innovation

• Adapts NPP for multiple historical datasets
• Links NPP discounting parameters to BHM
• Enables dynamic borrowing with ANPP