Control risk, don’t take it

One of the most important developments in clinical trial conduct during recent years is the advent of risk-based thinking, introducing a paradigm shift from controlling quality towards controlling risk. Since the release of ICH GCP E6 (R2) in 2017, sponsors are required to readily apply a risk-based quality management process to planning, execution and reporting of clinical trials. This implies a radical rethinking of traditional strategies for monitoring and quality control and introduces a host of conceptual, as well as practical difficulties that are often underestimated.

At palleos healthcare, an interdisciplinary team of subject matter experts has met these challenges by devising a lean quality risk management (QRM) process that is theoretically consistent, practically applicable, and fully compliant with regulatory requirements. Our approach is based on well-established QRM principles, as outlined in ICH Q9 and the ISO 31000. An emphasis is placed on scalability and added value to operational control during the execution of a trial.

QRM extends traditional quality management by explicitly addressing the effects of uncertainty. This entails prospectively accounting for deviations from the expected with regard to resources, study goals, critical data and processes. As such, QRM increases the robustness of clinical trial planning and demonstrably adds to achieving quality. At the same time, overall costs can be minimized without compromising quality by optimally allocating resources with regard to identified quality risks.

Central to our approach is a consistent formalization of risk based on mathematical decision theory and statistics. This affords optimal decision making and a high level of adaptivity throughout the clinical trial life cycle by being able to

fully assess any identified risk at all times using the information that accrues during execution of the trial

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make decisions with the desired level of confidence at the earliest point in time

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devise risk monitoring strategies that collect optimal information for decision making

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apply advanced statistical methods to the analysis of risk-related data and data collection for central monitoring

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