Statistical Modeling for Quality Risk Assessment of Clinical Trials: Follow-Up at the Era of Remote Auditing

Abstract

Background As investigator site audits have largely been conducted remotely during the COVID-19 pandemic, remote quality monitoring has gained some momentum. To further facilitate the conduct of remote quality assurance (QA) activities for clinical trials, we developed new quality indicators, building on a previously published statistical modeling methodology. Methods We modeled the risk of having an audit or inspection finding using historical audits and inspections data from 2011 to 2019. We used logistic regression to model finding risk for 4 clinical impact factor (CIF) categories: Safety Reporting, Data Integrity, Consent and Protecting Endpoints. Results We could identify 15 interpretable factors influencing audit finding risk of 4 out of 5 CIF categories. They can be used to realistically predict differences in risk between 25 and 43% for different sites which suffice to rank sites by audit and inspection finding risk. Conclusion Continuous surveillance of the identified risk factors and resulting risk estimates could be used to complement remote QA strategies for clinical trials and help to manage audit targets and audit focus also in post-pandemic times.

An Open-Source R Package for Detection of Adverse Events Under-Reporting in Clinical Trials: Implementation and Validation by the IMPALA (Inter coMPany quALity Analytics) Consortium

“…Nevertheless, AE under-reporting remains a common finding during audits and inspections [ 4 , 5 ]. Over the last few years, the industry has been exploring modern developments in data management and IT systems to facilitate cross-analysis of clinical studies [ 9 , 10 ]. In this analytical report, we aim to focus on the validation and reproducibility of an open-source analytics package that could be used across the ecosystem to tackle the issue of AE under-reporting.…”

An Open Source R Package for Detection of Adverse Events Under-Reporting in Clinical Trials - Implementation and Validation by the IMPALA (Inter coMPany quALity Analytics) Consortium

“…Nevertheless, AE under-reporting remains a common nding during audits and inspections [4,5]. Over the last few years, the industry has been exploring modern developments in data management and IT systems to facilitate cross-analysis of clinical studies [9,10]. In this analytical report, we aim to focus on the validation and reproducibility of an open-source analytics package that could be used across the ecosystem to tackle the issue of AE underreporting.…”

An Open Source R Package for Detection of Adverse Events Under-Reporting in Clinical Trials - Implementation and Validation by the IMPALA (Inter coMPany quALity Analytics) Consortium

“…Nevertheless, AE under-reporting remains a common nding during audits and inspections [4,5]. Over the last few years, the industry has been exploring modern developments in data management and IT systems to facilitate cross-analysis of clinical studies [9,10]. In this analytical report, we aimed to focus on the validation and reproducibility of an open-source analytics package that could be used across the ecosystem to tackle the issue of AE under-reporting.…”