Bayesian stopping guidelines for heart valve premarket approval studies.

Objectives: The Data Monitoring Committee (DMC) for the premarket approval (PMA) study of a new heart valve prosthesis convenes periodically to review the accumulating results of the study, and determines, among other things, whether there is enough concern with safety to stop the study. Their deliberations are largely subjective, based on their combined experience and expertise, but an objective aid to evaluating complication rates, usually called a stopping rule, is desirable.

Methods: The US Food and Drug Administration has designated objective performance criteria (OPC) for 7 heart valve complications. At the end of the PMA study, when approximately 800 patient-years have been accumulated, the complication rates must compare favorably with the OPC. Given the results to date at an interim review of the data, we use a Bayesian approach to compute the probability of passing the OPC test by the end of study.

Results: We provide a method that the DMC can use to predict the probability of passing the OPC test for each complication, and a graphical aid for each number of events, observed at 100 patient-year intervals.

Conclusions: Although the DMC ultimately uses combined experience and expertise to make the decision to stop a PMA valve study, we have provided an objective assessment of the probability of the valve ultimately passing the OPC test to aid in making that decision.