Bayesian adaptive design for device surveillance

Postmarket device surveillance studies often have important primary objectives tied to estimating a survival function at some future time $$T$$ with a certain amount of precision.

This article presents the details and various operating characteristics of a Bayesian adaptive design for device surveillance, as well as a method for estimating a sample size vector (determined by the maximum sample size and a preset number of interim looks) that will deliver the desired power.

We adopt a Bayesian adaptive framework, which recognizes the fact that persons enrolled in a study report their results over time, not all at once. At each interim look, we assess whether we expect to achieve our goals with only the current group or the achievement of such goals is extremely unlikely even for the maximum sample size.

Our Bayesian adaptive design can outperform two nonadaptive frequentist methods currently recommended by Food and Drug Administration (FDA) guidance documents in many settings.

Our method's performance can be sensitive to model misspecification and changes in the trial's enrollment rate.

The proposed design provides a more efficient framework for conducting postmarket surveillance of medical devices.