Statistical Monitoring in Clinical Trials: Best Practices for Detecting Data Anomalies Suggestive of Fabrication or Misconduct

Abstract

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.

Rethinking the Clinically Based Thresholds of TransCelerate BioPharma for Risk-Based Monitoring

“…It is worth noting that TransCelerate has published recent papers that place greater emphasis on statistical methods to identify anomalies and characterize risk, though they report that in a recent survey at least one third of risk indicators rely on static nonstatistical thresholds. [5][6][7][8] However, communicating statistical approaches, such as digit preference and Mahalanobis distance, to nonstatisticians can be challenging. The benefit of the proposed approach is that a variety of endpoints (eg, binary, count, normal [not shown]) and time considerations can be accommodated in the spirit of the original clinically based rules, and data visualization can be used to summarize and communicate risk.…”

“…4 Numerous regulatory guidance documents and papers have described risk-based approaches to identify quality and safety issues using centralized monitoring of the study database employing data visualization, sampling techniques, and other statistical methodologies. 2,[5][6][7][8][9][10][11][12][13] In particular, the position paper of TransCelerate BioPharma recommends that sponsors define risk thresholds to assess safety and quality risks based on past clinical experience. Using an example from the appendix of the position paper, consider a clinical trial in which a sponsor wants to assess safety risk by using the average number of adverse events per patient to perform comparisons between the clinical sites.…”

Risk-adapted monitoring is not inferior to extensive on-site monitoring: Results of the ADAMON cluster-randomised study

“…ADAMON had no influence on the quality of design or control of the extent of trial oversight and intensity of central monitoring in the participating trials. Recent recommendations 1 , 2 , 17 – 21 concerning trial oversight were not available at the time when ADAMON trials started.…”

Risk-Based Data Monitoring: Quality Control in Central Nervous System (CNS) Clinical Trials

“…Fundamental to RBDM is the uniform application of algorithms that identify either anomalous or discrepant data relationships or statistical outliers within POI. 24,25 These findings may then be addressed by the sponsor, as per the study statistical analysis plan. Multi-arity rules (ie, rules with multiple arguments) can be constructed using the total, subtotal, or individual domain scores of a given psychometric scale, comparing the domain scores within a particular scale or within one or more of the other scales administered in a clinical trial.…”