FDA and its stakeholders have an interest in assuring the integrity of clinical trial data and the protection of participants during the conduct of clinical research. Misconduct in clinical research, including, but not limited to the falsification or omission of data in reporting research results, places all subjects in that trial at possible safety risk. Fraud jeopardizes the reliability of data submitted to FDA, and undermines the Agency’s mission to protect and promote public health. FDA and other regulators rely on whistleblowers and site inspections to detect signs of possible misconduct.
Due to the volume of product submissions, FDA can only inspect a small proportion of clinical trial sites. The determination of which sites to inspect can involve recommendations by clinical and statistical reviewers, CDER’s risk-based site selection tool and FDA inspectors’ judgment and experiences.
This CRADA explores a data driven approach to selecting sites which exhibit data anomalies indicative of fraud, misconduct or sloppiness. Under this CRADA, FDA and CluePoints will develop and test enhancements to CluePoints existing software to produce an ordered list of “anomalous sites” (i.e. sites whose data are highly inconsistent with data from other sites); explore “moderators of treatment effect” (i.e. factors such as center, region or country that have a statistically significant impact on the magnitude of treatment effect); add statistical tests and models to those already in the existing software; refine the scoring system used to identify outlying centers; add an exploratory tool to identify moderators of treatment effect; test and implement the software in a high performance computing environment; and develop a user-friendly interface for use by medical reviewers and other interested parties at FDA.
Anticipated benefits to the FDA of the CRADA’s data driven approach include the detection of anomalous sites which may have escaped detection previously, rapid turnaround of results, the ability to determine the nature and extent of data anomalies and the ability to explore the interaction of various factors with data quality. These benefits are expected to not only accrue to the site inspection process and improve data quality for all reviewers, but may also inform the efforts of clinical and statistical reviewers to conduct sensitivity analyses, subgroup analyses and site by treatment effect explorations.