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

Abstract

Abstract Background The European Medicines Agency Good Pharmacovigilance Practices (GVP) guidelines provide a framework for pharmacovigilance (PV) audits, including limited guidance on risk assessment methods. Quality assurance (QA) teams of large and medium sized pharmaceutical companies generally conduct annual risk assessments of the PV system, based on retrospective review of data and pre-defined impact factors to plan for PV audits which require a high volume of manual work and resources. In addition, for companies of this size, auditing the entire “universe” of individual entities on an annual basis is generally prohibitive due to sheer volume. A risk assessment approach that enables efficient, temporal, and targeted PV audits is not currently available. Methods In this project, we developed a statistical model to enable holistic and efficient risk assessment of certain aspects of the PV system. We used findings from a curated data set from Roche operational and quality assurance PV data, covering a span of over 8 years (2011–2019) and we modeled the risk with a logistic regression on quality PV risk indicators defined as data stream statistics over sliding windows. Results We produced a model for each PV impact factor (e.g. 'Compliance to Individual Case Safety Report') for which we had enough features. For PV impact factors where modeling was not feasible, we used descriptive statistics. All the outputs were consolidated and displayed in a QA dashboard built on Spotfire®. Conclusion The model has been deployed as a quality decisioning tool available to Roche Quality professionals. It is used, for example, to inform the decision on which affiliates (i.e. pharmaceutical company commercial entities) undergo audit for PV activities. The model will be continuously monitored and fine-tuned to ensure its reliability.