A statistical approach to central monitoring of data quality in clinical trials

Venet, David; Doffagne, Erik; Burzykowski, Tomasz; Beckers, F.; Tellier, Yves; Genevois-Marlin, Eric; Becker, Ursula; Bée, Valerie; Wilson, Véronique; Legrand, Catherine; Buyse, Marc

doi:10.1177/1740774512447898

Cited by 84 publications

(108 citation statements)

References 13 publications

Supporting

Mentioning

106

Contrasting

Order By: Relevance

“…From a process perspective, the use of statistical methods [4][5][6] is essential to identify data anomalies. Digit preferences, for example, can signal potential data integrity concerns.…”

Section: Methodsmentioning

confidence: 99%

Defining a Central Monitoring Capability: Sharing the Experience of TransCelerate BioPharma’s Approach, Part 1

Wilson

Provencher

Gough

et al. 2014

Ther Innov Regul Sci

View full text Add to dashboard Cite

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.

show abstract

“…From a process perspective, the use of statistical methods [4][5][6] is essential to identify data anomalies. Digit preferences, for example, can signal potential data integrity concerns.…”

Section: Methodsmentioning

confidence: 99%

Defining a Central Monitoring Capability: Sharing the Experience of TransCelerate BioPharma’s Approach, Part 1

Wilson

Provencher

Gough

et al. 2014

Ther Innov Regul Sci

View full text Add to dashboard Cite

show abstract

“…53 Centers with outrageous information irregularity scores are deserving of further examination, with the point of clarifying the distinctions, retraining the site staff if required, or -in the most dire outcome imaginable -to reveal a misrepresentation that would some way or another stay undetected. Hence high data inconsistency scores are a statistical finding with no implied value judgment and should not be interpreted as a "data quality" index.…”

Section: ) Use Of Technologymentioning

confidence: 99%

Qualitative features in clinical trials: coordinates for prevention of passive and active misconduct

et al. 2018

View full text Add to dashboard Cite

For many years, the quality concept in clinical trials has been discussed and recommended by Good Clinical Practice (GCP) guidelines. Regulatory Authorities and also the Public Involvement anticipate that the pharmaceutical industry will concentrate on creating quality frameworks amid the arranging and leading of conventions of controlled protocols. Nevertheless, many factors have been suggested as contributing to the occurrence of scientific misconduct within the research field, such as: personal and financial interests, site monitoring, available resources, workload, competition among investigators, and the implicit consent of sponsors. The negligence on data fraud represents not only omission but misconduct as well, in this case, a passive attitude intrinsically related to the act of transgression. A properly culture of research must be based on a fundamental ethos of integrity, openness and honest work of high quality in all parts of the research process. There is a need to change the focus from inspection-based quality improvement to planned systematic quality management within clinical trials. In search for a monitoring improvement, a full statistical way to deal with information recognition comprises of executing however many measurable tests as could be allowed on whatever number clinical information factors as could be expected under varied circumstances. Adoption of specific and preventive clinical trial monitoring procedures can identify potential misconduct and data fraud leading to improvement in overall data quality and scientific reports.

show abstract

“…Building on prior research, [3][4][5][6][8][9][10] TransCelerate tested statistical-monitoring methods on a biopharmaceutical industry-sponsored clinical study database under conditions partially mimicking those found during real-world study conduct. The objective of this experiment was to detect data anomalies suggestive of noncompliance, but it primarily focused on intentionally fabricated data because of the potential difficulty in detecting this type of noncompliance.…”

Section: Introductionmentioning

confidence: 99%

“…1,2 One strategy being used is statistical monitoring, or the use of various statistical methods during study conduct to detect data anomalies suggestive of fabrication or noncompliance. 1,[3][4][5] The multicentric nature of most clinical trials offers an opportunity to check the plausibility of data from one site against data from all other sites. 3 Statistical monitoring relies on the highly structured nature of data because each protocol is expected to be implemented consistently at all sites.…”

Section: Introductionmentioning

confidence: 99%

“…3 Statistical monitoring relies on the highly structured nature of data because each protocol is expected to be implemented consistently at all sites. 4 Statistical checks are powerful tools because the multivariate structure and/or time dependence of variables are sensitive to deviations and hard to mimic. Fabricated data, even if plausible from a univariate perspective, are likely to exhibit abnormal multivariate patterns that are detectable statistically.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Knepper¹,

Lindblad

Sharma

et al. 2016

Ther Innov Regul Sci

View full text Add to dashboard Cite

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.

show abstract

A statistical approach to central monitoring of data quality in clinical trials

Abstract: Central statistical monitoring can both optimize on-site monitoring and improve data quality and as such provides a cost-effective way of meeting regulatory requirements for clinical trials.

Cited by 84 publications

References 13 publications

Defining a Central Monitoring Capability: Sharing the Experience of TransCelerate BioPharma’s Approach, Part 1

Defining a Central Monitoring Capability: Sharing the Experience of TransCelerate BioPharma’s Approach, Part 1

Qualitative features in clinical trials: coordinates for prevention of passive and active misconduct

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

Contact Info

Product

Resources

About