Deep Historical Borrowing Framework to Prospectively and Simultaneously Synthesize Control Information in Confirmatory Clinical Trials with Multiple Endpoints

TL;DR

This paper introduces a deep learning-based Bayesian hierarchical framework for prospectively synthesizing control information across multiple endpoints in clinical trials, ensuring error control and robust decision-making.

Contribution

It develops a novel method combining Bayesian hierarchical modeling with deep learning to handle multiple endpoints simultaneously in confirmatory trials.

Findings

Proper control of family-wise error rate demonstrated

Method preserves power under prior-data conflict

Effective in a case study on Immunology

Abstract

In current clinical trial development, historical information is receiving more attention as it provides utility beyond sample size calculation. Meta-analytic-predictive (MAP) priors and robust MAP priors have been proposed for prospectively borrowing historical data on a single endpoint. To simultaneously synthesize control information from multiple endpoints in confirmatory clinical trials, we propose to approximate posterior probabilities from a Bayesian hierarchical model and estimate critical values by deep learning to construct pre-specified strategies for hypothesis testing. This feature is important to ensure study integrity by establishing prospective decision functions before the trial conduct. Simulations are performed to show that our method properly controls family-wise error rate (FWER) and preserves power as compared with a typical practice of choosing constant critical values given a subset of null space. Satisfactory performance under prior-data conflict is also demonstrated. We further illustrate our method using a case study in Immunology.