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.
Keywords: Advanced analytics; Audit; Clinical trials; Good clinical practice (GCP); Quality assurance; Statistical modeling.
【저자키워드】 clinical trials, Quality assurance, Advanced analytics, Audit, Good clinical practice (GCP), Statistical modeling., 【초록키워드】 clinical trial, Safety, COVID-19 pandemic, clinical trials, risk, complement, risk factor, activity, clinical, Logistic regression, Reporting, clinical impact, target, Consent, methodology, Quality, estimate, predict, Quality assurance, endpoints, Audit, Good Clinical Practice, continuous surveillance, impact factor, Factor, help, categories, investigator, integrity, data integrity, statistical, Quality monitoring, resulting, identify, conducted, facilitate, can be used, Protecting, 【제목키워드】 clinical, modeling, remote, Quality, assessment,