Harnessing the Power of Quality Assurance Data: Can We Use Statistical Modeling for Quality Risk Assessment of Clinical Trials?

Abstract

Background The increasing number of clinical trials and their complexity make it challenging to detect and identify clinical quality issues timely. Despite extensive sponsor audit programs and monitoring activities, issues related to data integrity, safety, sponsor oversight and patient consent have recurring audit and inspection findings. Recent developments in data management and IT systems allow statistical modeling to provide insights to clinical Quality Assurance (QA) professionals to help mitigate some of the key clinical quality issues more holistically and efficiently. Methods We used findings from a curated data set from Roche/Genentech operational and quality assurance study data, covering a span of 8 years (2011-2018) and grouped them into 5 clinical impact factor categories, for which we modeled the risk with a logistic regression using hand crafted features. Results We were able to train 5 interpretable, cross-validated models with several distinguished risk factors, many of which confirmed field observations of our quality professionals. Our models were able to reliably predict a decrease in risk by 12-44%, with 2-8 coefficients each, despite a low signal-to-noise ratio in our data set. Conclusion We proposed a modeling strategy that could provide insights to clinical QA professionals to help them mitigate key clinical quality issues (e.g., safety, consent, data integrity) in a more sustained data-driven way, thus turning the traditional reactive approach to a more proactive monitoring and alerting approach. Also, we are calling for cross-sponsors collaborations and data sharing to improve and further validate the use of statistical models in clinical QA.

Using Statistical Modeling for Enhanced and Flexible Pharmacovigilance Audit Risk Assessment and Planning

“…volume of ICSRs) were also reflective of the Roche product portfolio. Further validation (using high level principles of our model) on other pharmaceutical companies data could be feasible but would require cross-company collaborations and data sharing like in the GCP arena [ 11 ].…”

Building data analytics skills for clinical drug development quality professionals - the Data Analytics University training program

“…While the industry has recently been trying to leverage modern developments in data management and IT systems to facilitate the cross-analysis of clinical studies and PV processes [5,6,7,8,9,10,11], a unique skill set was needed to build and embed advanced analytics capabilities within its staff. Although clinical QA experts bring a unique skillset, "data literacy" is becoming a necessary core capability for the QA professional of the future.…”

Letter to the Editor: New Approaches to Regulatory Innovation Emerging During the Crucible of COVID-19

“…We believe that other important considerations contributing to regulatory innovation in clinical drug development should be highlighted. For example, new ways of delivering quality assurance (QA) for clinical trials, by leveraging advanced analytics, had been developed in the recent years [2]. Other publications also emphasized the need to perform QA differently to address challenges posed by the pandemic and changing quality "to prevention rather than correction, critical thinking vs. editing, root cause analysis vs. symptom identification, systemic vs. isolated errors, partnering vs. policing, and applying solutions vs. fixing individual issues" [3].…”

“…The increasing number of clinical trials and the growing complexity of study designs make it challenging to detect and identify clinical quality issues timely. Late detection of quality issues can lead to delayed filing, delayed approval and delayed patient access to innovative therapies [2]. Furthermore, the restrictions due the pandemic (e.g.…”

“…To transition from the experimentation phase that happened during the pandemic to the implementation of routine, remote and analytics-driven QA operations, sponsors and agencies will have to continue to collaborate and address challenges such as: fit-for-purpose Information Technology (IT) infrastructures, automation, cross-company QA data sharing, QA staff data literacy and models validation [2,5]. While the COVID-19 pandemic accelerated the adoption of new ways of working and pushed innovation further in the regulatory space, it also brought new rationales for a change in the QA paradigm, i.e.…”