Detecting Data Quality Issues in Clinical Trials: Current Practices and Recommendations

Knepper, David; Fenske, Christian; Nadolny, Patrick; Bedding, Alun; Грибкова, Е.И.; Polzer, John; Neumann, Jennifer; Wilson, Brad; Benedict, Joanne; Lawton, Andy

doi:10.1177/2168479015620248

Cited by 19 publications

(12 citation statements)

References 20 publications

(44 reference statements)

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Zink

Dmitrienko

Dmitrienko³

2018

Ther Innov Regul Sci

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zink

Dmitrienko

Dmitrienko³

2018

Ther Innov Regul Sci

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“…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.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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BackgroundAccording to Good Clinical Practice, clinical trials must protect rights and safety of patients and make sure that the trial results are valid and interpretable. Monitoring on-site has an important role in achieving these objectives; it controls trial conduct at trial sites and informs the sponsor on systematic problems. In the past, extensive on-site monitoring with a particular focus on formal source data verification often lost sight of systematic problems in study procedures that endanger Good Clinical Practice objectives. ADAMON is a prospective, stratified, cluster-randomised, controlled study comparing extensive on-site monitoring with risk-adapted monitoring according to a previously published approach.MethodsIn all, 213 sites from 11 academic trials were cluster-randomised between extensive on-site monitoring (104) and risk-adapted monitoring (109). Independent post-trial audits using structured manuals were performed to determine the frequency of major Good Clinical Practice findings at the patient level. The primary outcome measure is the proportion of audited patients with at least one major audit finding. Analysis relies on logistic regression incorporating trial and monitoring arm as fixed effects and site as random effect. The hypothesis was that risk-adapted monitoring is non-inferior to extensive on-site monitoring with a non-inferiority margin of 0.60 (logit scale).ResultsAverage number of monitoring visits and time spent on-site was 2.1 and 2.7 times higher in extensive on-site monitoring than in risk-adapted monitoring, respectively. A total of 156 (extensive on-site monitoring: 76; risk-adapted monitoring: 80) sites were audited. In 996 of 1618 audited patients, a total of 2456 major audit findings were documented. Depending on the trial, findings were identified in 18%–99% of the audited patients, with no marked monitoring effect in any of the trials. The estimated monitoring effect is −0.04 on the logit scale with two-sided 95% confidence interval (−0.40; 0.33), demonstrating that risk-adapted monitoring is non-inferior to extensive on-site monitoring. At most, extensive on-site monitoring could reduce the frequency of major Good Clinical Practice findings by 8.2% compared with risk-adapted monitoring.ConclusionCompared with risk-adapted monitoring, the potential benefit of extensive on-site monitoring is small relative to overall finding rates, although risk-adapted monitoring requires less than 50% of extensive on-site monitoring resources. Clusters of findings within trials suggest that complicated, overly specific or not properly justified protocol requirements contributed to the overall frequency of findings. Risk-adapted monitoring in only a sample of patients appears sufficient to identify systematic problems in the conduct of clinical trials. Risk-adapted monitoring has a part to play in quality control. However, no monitoring strategy can remedy defects in quality of design. Monitoring should be embedded in a comprehensive quality management approach covering the entire t...

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“…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.…”

Section: System Requirementsmentioning

confidence: 99%

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

McNamara

Engelhardt

Potter

et al. 2019

Ther Innov Regul Sci

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Monitoring the quality of clinical trial efficacy outcome data has received increased attention in the past decade, with regulatory guidance encouraging it to be conducted proactively, and remotely. However, the methods utilized to develop and implement risk-based data monitoring (RBDM) programs vary, and there is a dearth of published material to guide these processes in the context of central nervous system (CNS) trials. We reviewed regulatory guidance published within the past 6 years, generic white papers, and studies applying RBDM to data from CNS clinical trials. Methodologic considerations and system requirements necessary to establish an effective, real-time risk-based monitoring platform in CNS trials are presented. Key RBDM terms are defined in the context of CNS trial data, such as "critical data," "risk indicators," "noninformative data," and "mitigation of risk." Additionally, potential benefits of, and challenges associated with implementation of data quality monitoring are highlighted. Application of methodological and system requirement considerations to real-time monitoring of clinical ratings in CNS trials has the potential to minimize risk and enhance the quality of clinical trial data.

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Detecting Data Quality Issues in Clinical Trials: Current Practices and Recommendations

Cited by 19 publications

References 20 publications

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

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

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

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

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