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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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Published on: July 24, 2019

A pragmatic approach for dynamically incorporating predicate device data in prospective diagnostic test studies.

Graeme L Hickey¹, Valentin Parvu¹, Yongqiang Zhang²

¹Becton, Dickinson and Company, Franklin Lakes, New Jersey, USA.

Journal of Biopharmaceutical Statistics

|June 1, 2022

Summary

This study introduces a dynamic Bayesian method to incorporate historical data into new diagnostic test trials. This approach can reduce sample sizes and trial duration for developing accurate diagnostic tests.

Keywords:

Augmentation Bayesian diagnostic tests sensitivity

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Area of Science:

Medical Diagnostics
Biostatistics
Clinical Trial Design

Background:

Clinical studies are essential for validating new diagnostic tests.
Historical data from predicate devices can potentially optimize new test evaluation.
Reducing sample size and trial duration are key objectives in clinical study design.

Purpose of the Study:

To propose a dynamic Bayesian method for incorporating historical data into new diagnostic test studies.
To reduce sample size and trial duration by leveraging existing data.
To enable adaptive trial frameworks for early stopping based on success.

Main Methods:

Utilizing the Bayesian power prior method with a dynamically calculated power parameter.
Comparing historical and new data using a one-sided comparison.
Employing a scaled-Weibull discount function to adjust the effective sample size borrowed.
Integrating the method within an adaptive trial framework for early success termination.

Main Results:

The proposed dynamic method allows for effective down-weighting of historical data.
The approach is pragmatic and conservative, ensuring data integrity.
The method is demonstrated with an example for detecting Methicillin-resistant Staphylococcus aureus (MRSA).

Conclusions:

The dynamic Bayesian approach offers a viable strategy to optimize clinical trials for new diagnostic tests.
Incorporating historical data can lead to more efficient study designs.
This method supports the development of accurate diagnostic tools with reduced resource utilization.