Bayesian Modeling for the Detection of Adverse Events Underreporting in Clinical Trials.

INTRODUCTION: Safety underreporting is a recurrent issue in clinical trials that can impact patient safety and data integrity. Clinical quality assurance (QA) practices used to detect underreporting rely on on-site audits; however, adverse events (AEs) underreporting remains a recurrent issue. In a recent project, we developed a predictive model that enables oversight of AE reporting for clinical quality program leads (QPLs). However, there were limitations to using solely a machine learning model. OBJECTIVE: Our primary objective was to propose a robust method to compute the probability of AE underreporting that could complement our machine learning model. Our model was developed to enhance patients' safety while reducing the need for on-site and manual QA activities in clinical trials. METHODS: We used a Bayesian hierarchical model to estimate the site reporting rates and assess the risk of underreporting. We designed the model with Project Data Sphere clinical trial data that are public and anonymized. RESULTS: We built a model that infers the site reporting behavior from patient-level observations and compares them across a study to enable a robust detection of outliers between clinical sites. CONCLUSION: The new model will be integrated into the current dashboard designed for clinical QPLs. This approach reduces the need for on-site audits, shifting focus from source data verification to pre-identified, higher risk areas. It will enhance further QA activities for safety reporting from clinical trials and generate quality evidence during pre-approval inspections.

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

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.

Harnessing the Power of Quality Assurance Data: Can We Use Statistical Modeling for Quality Risk Assessment of Clinical Trials?

BACKGROUND: The increasing number of clinical trials and their complexity make it challenging to detect and identify clinical quality issues timely. Despite extensive sponsor audit programs and monitoring activities, issues related to data integrity, safety, sponsor oversight and patient consent have recurring audit and inspection findings. Recent developments in data management and IT systems allow statistical modeling to provide insights to clinical Quality Assurance (QA) professionals to help mitigate some of the key clinical quality issues more holistically and efficiently. METHODS: We used findings from a curated data set from Roche/Genentech operational and quality assurance study data, covering a span of 8 years (2011-2018) and grouped them into 5 clinical impact factor categories, for which we modeled the risk with a logistic regression using hand crafted features. RESULTS: We were able to train 5 interpretable, cross-validated models with several distinguished risk factors, many of which confirmed field observations of our quality professionals. Our models were able to reliably predict a decrease in risk by 12-44%, with 2-8 coefficients each, despite a low signal-to-noise ratio in our data set. CONCLUSION: We proposed a modeling strategy that could provide insights to clinical QA professionals to help them mitigate key clinical quality issues (e.g., safety, consent, data integrity) in a more sustained data-driven way, thus turning the traditional reactive approach to a more proactive monitoring and alerting approach. Also, we are calling for cross-sponsors collaborations and data sharing to improve and further validate the use of statistical models in clinical QA.

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

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.