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Bayesian adaptive design for device surveillance.

Thomas A Murray¹, Bradley P Carlin, Theodore C Lystig

¹Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.

Clinical Trials (London, England)

|November 29, 2012

Summary

This summary is machine-generated.

This study introduces a Bayesian adaptive design for medical device surveillance, offering a more efficient approach than traditional methods. The design helps estimate survival functions with desired precision, improving postmarket surveillance strategies.

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Area of Science:

Biostatistics
Medical Device Regulation
Clinical Trial Design

Background:

Postmarket device surveillance studies are crucial for estimating survival functions at future time points with specific precision.
Accurate estimation of survival functions is essential for assessing long-term medical device performance and safety.

Purpose of the Study:

To present a Bayesian adaptive design for device surveillance, detailing its operating characteristics.
To introduce a method for estimating sample size vectors to achieve desired statistical power in adaptive trials.

Main Methods:

Utilizes a Bayesian adaptive framework that accounts for the sequential reporting of results in studies.
Incorporates interim looks to assess the likelihood of achieving study goals based on current data and maximum sample size.

Main Results:

The proposed Bayesian adaptive design demonstrates superior performance compared to nonadaptive frequentist methods in many scenarios.
Outperforms current methods recommended by Food and Drug Administration (FDA) guidance documents.

Conclusions:

The Bayesian adaptive design offers a more efficient framework for conducting postmarket surveillance of medical devices.
Performance may be sensitive to model misspecification and variations in patient enrollment rates.