Enabling Data-Driven Clinical Quality Assurance: Predicting Adverse Event Reporting in Clinical Trials Using Machine Learning

Timothé Ménard (), Yves Barmaz, Björn Koneswarakantha, Rich Bowling and Leszek Popko
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Timothé Ménard: F. Hoffmann-La Roche
Yves Barmaz: F. Hoffmann-La Roche
Björn Koneswarakantha: F. Hoffmann-La Roche
Rich Bowling: Genentech - A Member of the Roche group
Leszek Popko: F. Hoffmann-La Roche

Drug Safety, 2019, vol. 42, issue 9, No 3, 1045-1053

Abstract: Abstract Introduction Adverse event (AE) under-reporting has been a recurrent issue raised during health authorities Good Clinical Practices (GCP) inspections and audits. Moreover, safety under-reporting poses a risk to patient safety and data integrity. The current clinical Quality Assurance (QA) practices used to detect AE under-reporting rely heavily on investigator site and study audits. Yet several sponsors and institutions have had repeated findings related to safety reporting, and this has led to delays in regulatory submissions. Recent developments in data management and IT systems allow data scientists to apply techniques such as machine learning to detect AE under-reporting in an automated fashion. Objective In this project, we developed a predictive model that enables Roche/Genentech Quality Program Leads oversight of AE reporting at the program, study, site, and patient level. This project was part of a broader effort at Roche/Genentech Product Development Quality to apply advanced analytics to augment and complement traditional clinical QA approaches. Method We used a curated data set from 104 completed Roche/Genentech sponsored clinical studies to train a machine learning model to predict the expected number of AEs. Our final model used 54 features built on patient (e.g., demographics, vitals) and study attributes (e.g., molecule class, disease area). Results In order to evaluate model performance, we tested how well it would detect simulated test cases based on data not used for model training. For relevant simulation scenarios of 25%, 50%, and 75% under-reporting on the site level, our model scored an area under the curve (AUC) of the receiver operating characteristic (ROC) curve of 0.62, 0.79, and 0.92, respectively. Conclusion The model has been deployed to evaluate safety reporting performance in a set of ongoing studies in the form of a QA/dashboard cockpit available to Roche Quality Program Leads. Applicability and production performance will be assessed over the next 12–24 months in which we will develop a validation strategy to fully integrate our model into Roche QA processes.

Date: 2019
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DOI: 10.1007/s40264-019-00831-4

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