ArticleTherapeutic innovation & regulatory science2021
Using Statistical Modeling for Enhanced and Flexible Pharmacovigilance Audit Risk Assessment and Planning.
Article in Therapeutic innovation & regulatory science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Pharmacovigilance - Technological Advancements, Recent Developments and Innovations.Current drug safety · 2025Review
- Evaluating the effectiveness of AI-enhanced "One Body, Two Wings" pharmacovigilance models in China: a nationwide survey on medication safety and risk management.Frontiers in health services · 2025Article
- Pharmacovigilance in China: Evolution and future challenges.British journal of clinical pharmacology · 2023Review
- Statistical Modeling for Quality Risk Assessment of Clinical Trials: Follow-Up at the Era of Remote Auditing.Therapeutic innovation & regulatory science · 2022Article
- Clinical Quality Considerations when Using Next-Generation Sequencing (NGS) in Clinical Drug Development.Therapeutic innovation & regulatory science · 2021Review
- Leveraging analytics to assure quality during the Covid-19 pandemic - The COVACTA clinical study example.Contemporary clinical trials communications · 2020Article
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Authors and funding
5 authors.
Funding
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Abstract
backgroundThe 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.
methodsIn 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.
resultsWe 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
conclusionThe 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.
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