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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

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Digital twins and Bayesian dynamic borrowing: Two recent approaches for incorporating historical control data.

Carl-Fredrik Burman^1,2, Erik Hermansson¹, David Bock¹

¹Early Biometrics & Statistical Innovation, Data Science & Artificial Intelligence, R&D, AstraZeneca, Gothenburg, Sweden.

Pharmaceutical Statistics

|March 4, 2024

Summary

Bayesian dynamic borrowing (BDB) and digital twins (DT) use historical data in clinical trials. While DT shows promise, BDB may inflate type 1 errors, requiring careful consideration for real-world applications.

Keywords:

PROCOVA™clinical trials machine learning prognostic score robust mixture prior

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Area of Science:

Biostatistics
Clinical Trial Design
Health Informatics

Background:

Increasing interest in using external control data for randomized clinical trials (RCTs).
Potential benefits include reduced costs, shorter trial durations, and feasibility for small populations.
Bayesian dynamic borrowing (BDB) and Digital Twins (DT) are emerging methods for historical data utilization.

Purpose of the Study:

To analyze and compare Bayesian dynamic borrowing (BDB) and Digital Twins (DT) methods for RCTs.
To evaluate their performance using analytic derivations and simulations.
To identify fundamental differences and practical considerations for their application.

Main Methods:

Analytic derivations and simulation studies were employed.
Bayesian dynamic borrowing (BDB) approach was examined.
Digital Twins (DT) method, utilizing prognostic scores from historical data within an ANCOVA framework, was analyzed.

Main Results:

Both BDB and DT aim to leverage historical data but possess distinct underlying mechanisms.
A significant concern identified for BDB is the potential inflation of type 1 error rates.
The tangible benefits of DT in actual randomized clinical trials require further empirical validation.

Conclusions:

BDB and DT, despite similar goals, have critical differences impacting their suitability for specific RCTs.
Type 1 error inflation is a notable drawback of BDB that necessitates careful management.
Further research and evidence are needed to establish the practical value and reliability of DT in real-world clinical trial settings.