Statistical Methods for Clinical Study Site Selection

Abstract

Background:

The US Food and Drug Administration conducts on-site inspections and data audits through Bioresearch Monitoring program for assurance of the quality and integrity of data in the pre- and postapproval processes. It is important to inspect the study sites that are different compared with other sites in clinical studies and identify the problems related to those sites. Usually one cannot inspect all the sites in a clinical study because of limited resources, and statistical tools are needed to help in selecting sites for inspection.

Methods:

We propose two technical approaches, namely Fisher combination approach and likelihood ratio test (LRT) approach, for site selection, with each approach integrating the information obtained from a P value matrix. The proposed approaches produce site rankings, and the sites with highest rankings may be selected for inspection.

Results:

The application of the approaches is demonstrated through a hypothetical data set reflecting the pattern of the real data in a premarket approval submission for a diagnostic device. The proposed methods are shown, through extensive simulations, to control false discovery rate, while maintaining good sensitivity.

Conclusion:

The proposed approaches will be useful for site selection process. However, limitations exist when only using the statistical approaches proposed here. In practice, investigators will select the site for inspection by considering the outputs from the statistical approaches along with other important factors. Future research topic is discussed to facilitate practical application of the approaches.