Background: Traditional site-monitoring techniques are not optimal in finding data fabrication and other nonrandom data distributions with the greatest potential for jeopardizing the validity of study results. TransCelerate BioPharma conducted an experiment testing the utility of statistical methods for detecting implanted fabricated data and other signals of noncompliance. Methods: TransCelerate tested statistical monitoring on a data set from a chronic obstructive pulmonary disease (COPD) clinical study with 178 sites and 1554 subjects. Fabricated data were selectively implanted in 7 sites and 43 subjects by expert clinicians in COPD. The data set was partitioned to simulate studies of different sizes. Analyses of vital signs, spirometry, visit dates, and adverse events included distributions of standard deviations, correlations, repeated values, digit preference, and outlier/inlier detection. An interpretation team, including clinicians, statisticians, site monitoring, and data management, reviewed the results and created an algorithm to flag sites for fabricated data. Results: The algorithm identified 11 sites (19%), 19 sites (31%), 28 sites (16%), and 45 sites (25%) as having potentially fabricated data for studies 2A, 2, 1A, and 1, respectively. For study 2A, 3 of 7 sites with fabricated data were detected, 5 of 7 were detected for studies 2 and 1A, and 6 of 7 for study 1. Except for study 2A, the algorithm had good sensitivity and specificity (>70%) for identifying sites with fabricated data. Conclusions: We recommend a crossfunctional, collaborative approach to statistical monitoring that can adapt to study design and data source and use a combination of statistical screening techniques and confirmatory graphics.

Central monitoring, on-site monitoring, and off-site monitoring provide an integrated approach to clinical trial quality management. TransCelerate distinguishes central monitoring from other types of central data review activities and puts it in the context of an overall monitoring strategy. Any organization seeking to implement central monitoring will need people with the right skills, technology options that support a holistic review of study-related information, and adaptable processes. There are different approaches actively being used to implement central monitoring. This article provides a description of how companies are deploying central monitoring, as well as samples of the workflows that illustrate how some have implemented it. The desired outcomes include earlier, more predictive detection of quality issues. This paper describes the initial implementation steps designed to learn what organizational capabilities are necessary.

Background: TransCelerate's model approach to risk-based monitoring (RBM) includes the application of the appropriate monitoring activities to enable both the early detection and timely resolution of issues. This article is a follow-up to part 1, published in the September 2014 issue with the same title. Methods: The intent of this paper is to share information on what has been learned by various companies' applications of central monitoring activities based on different RBM operating models. A library of risk indicators has been created, and this paper provides additional guidance on what has been learned in the application of these tools. Results: The goal is to share the needs related to people, process, and technology as experienced by TransCelerate member companies. Conclusions: One of the primary issue detection methods of central monitoring is the proactive identification of areas of focus through the use of risk indicators.

TransCelerate recommends the creation of an integrated, multifaceted approach to proactively detect data quality issues. Detection methods should include a strategy tailored to the characteristics of the study. Some sponsors are taking advantage of more advanced methods and integrated processes and systems to proactively detect and address issues, relying on advances in technology to more efficiently review data in real time. Further research is underway to assess statistical data quality detection methodology in clinical trials.