TransCelerate has developed a risk-based monitoring methodology that transforms clinical trial monitoring from a model rooted in source data verification (SDV) to a comprehensive approach leveraging cross-functional risk assessment, technology, and adaptive on-site, off-site, and central monitoring activities to ensure data quality and subject safety. Evidence suggests that monitoring methods that concentrate on what is critical for a study and a site may produce better outcomes than do conventional SDV-driven models. This article assesses the value of SDV in clinical trial monitoring via a literature review, a retrospective analysis of data from clinical trials, and an assessment of major and critical findings from TransCelerate member company internal audits. The results support the hypothesis that generalized SDV has limited value as a quality control measure and reinforce the value of other risk-based monitoring activities.

The International Council for Harmonisation (ICH) E6(R2) (International Council for Harmonisation (ICH). ICH harmonised guideline: integrated addendum to ICH E6(R1): guideline for good clinical practice E6(R2). 2016. https ://datab ase. ich.org/sites /defau lt/files /E6_R2_Adden dum.pdf. Accessed 5 Dec 2019) introduced Quality Tolerance Limits (QTLs) to the industry, and in doing so, modernized quality control for clinical trials. QTLs provide measured feedback on clinical trial parameters previously only used by statistical and clinical functions to track trial progress toward endpoints. Elevating these measures as part of the Quality Management System (QMS) provides greater visibility across clinical trial functions and the enterprise as well as to measures that are important indicators of the state of participant protection and reliability of trial results. In support of this new requirement, TransCelerate developed a framework to guide industry sponsors and their agents in implementing QTLs. This QTL Framework is intended to aid industry's ability to improve the quality of clinical research through the implementation of QTLs in a way that helps protect trial participants and reliability of trial results while meeting Health Authority (HA) expectations. The framework is intended to maximize efficiency and minimize confusion in the implementation of QTLs. The framework includes proposed approaches for implementation of QTLs for a clinical trial as defined in Section 5.0.4 and 5.0.7 of ICH E6(R2) (International Council for Harmonisation (ICH). ICH harmonised guideline: integrated addendum to ICH E6(R1): guideline for good clinical practice E6(R2). 2016. https ://datab ase.ich.org/ sites /defau lt/files /E6_R2_Adden dum.pdf. Accessed 5 Dec 2019) and considerations for setting thresholds.

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.

Background In 2016, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use updated its efficacy guideline for good clinical practice and introduced quality tolerance limits (QTLs) as a quality control in clinical trials. Previously, TransCelerate proposed a framework for QTL implementation and parameters. Historical data can be important in helping to determine QTL thresholds in new clinical trials. Methods This article presents results of historical data analyses for the previously proposed parameters based on data from 294 clinical trials from seven TransCelerate member companies. The differences across therapeutic areas were assessed by comparing Alzheimer’s disease (AD) and oncology trials using a separate dataset provided by Medidata. Results TransCelerate member companies provided historical data on 11 QTL parameters with data sufficient for analysis for parameters. The distribution of values was similar for most parameters with a relatively small number of outlying trials with high parameter values. Medidata provided values for three parameters in a total of 45 AD and oncology trials with no obvious differences between the therapeutic areas. Conclusion Historical parameter values can provide helpful benchmark information for quality control activities in future trials.