The following secure clinical trial data sharing case study based on real work conducted between TrialAssure and one of its trusted sponsor partners.

Problem 

A clinical stage biotech company, sought to share their clinical trial datasets voluntarily with research partners for secondary analyses and derivative research. However, they faced significant challenges, including the high risk of re-identification due to a small patient population that makes traditional qualitative anonymization insufficient. Additionally, they needed to protect patient privacy while preserving the data’s utility for meaningful research, as well as maintain compliance with varying global regulatory requirements.  

To address these complexities, they sought an experienced partner capable of delivering quantitative, evidence-based anonymization. 

Solution 

TrialAssure, with its industry-leading AI-powered platform ANONYMIZE, was selected as the ideal partner to meet these challenges. Leveraging over a decade of experience in clinical data transparency and anonymization, TrialAssure delivered a comprehensive solution tailored to their needs, starting with a risk-based anonymization approach. 

TrialAssure ANONYMIZE employed a sophisticated quantitative anonymization strategy to achieve measurable re-identification risk thresholds while maintaining optimal data utility. Through TrialAssure’s quick and efficient processes, the end-to-end project, encompassing risk analysis, anonymization, and reporting, was completed approximately 80 percent faster.  

On top of the anonymized dataset, TrialAssure provided a detailed Data Anonymization Report and Data Specification Report to ensure visibility into the anonymization process, support informed decision-making, and demonstrate compliance with regulatory standards. Additionally, TrialAssure ANONYMIZE acted as a secure data exchange platform, enabling seamless and protected data sharing with the clinical-stage biotech’s research partners. 

Results 

This entire project, including Quantitative Risk Analysis, Variable-Level Anonymization Strategy, and Post-Anonymization Risk Assessment, was completed within an accelerated 1-week timeline—a process that would have taken significantly longer using manual methods.  

By automating key steps, TrialAssure significantly reduced the time and resources required, providing optimal value and cost savings to the clinical-stage biotech. The company achieved measurable re-identification risk thresholds with statistical confidence, ensuring robust patient privacy and regulatory compliance. Following the success of this project, they continue to partner with TrialAssure for their secure clinical trial data sharing needs, supporting ongoing voluntary data sharing initiatives.