TECHNOLOGY & DATA MANAGEMENT


higher-risk critical elements essential for topline study reporting, while deferring reviews of lower-risk data to a later stage.


Addressing Low-Detectability Risks Identifying the impact and probability of errors is often more straightforward than assessing their detectability, making detectability a critical area for innovation. Key challenges include errors during patient screening and eligibility determination, which can lead to wrongful enrollment; misreported adverse events caused by diagnostic inaccuracies; undetected deviations in sample collection and processing; and intentional data fabrication or manipulation. These high-risk events are particularly prevalent in decentralized trials lacking rigorous monitoring or validation technologies, but they also pose significant threats to the integrity of any study design with inadequate safeguards and risk mitigation strategies. In the future, clinical trialists should prioritize developing new approaches and technologies that effectively address low-detectability events using data-validated methods to enhance risk mitigation.


Conclusion This model serves as a hypothetical framework designed to address the needs of all stakeholders, balancing cost-efficiency, data quality, and regulatory compliance in clinical trials. It emphasizes a risk-focused approach to demonstrate cost-effectiveness. By aligning with FDA guidance and adopting risk-based principles, sponsors can optimize resource allocation, lower monitoring costs, and produce high-quality data to support critical funding milestones, thereby enhancing the feasibility of clinical research for start-ups navigating the competitive biotech industry.


About the Author: For 20 years, Peter Gompper has delivered consulting services to biopharma organizations, specializing in clinical R&D and operational optimization. Currently, he focuses on developing advanced sensor technologies that enhance data validation, improve patient outcomes, and drive cost efficiencies in healthcare.


Correspondence: pgo@att.net


By combining base cost analysis with data variable prioritization, sponsors can develop an optimal portfolio of actions to reduce uncertainty and enhance efficiency


Credit: THICHA SATAPITANON via Shutterstock. Outsourcing in Clinical Trials Handbook | 41


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68