Tim Rolfe, Chris Wells, Maciej Fronc.

Three presentations: (1) Harnessing the power of centralized statistical monitoring in post pandemic trial conduct [Tim Rolfe] (2) Quality Tolerance Limits: Industry Status, Challenges, Impact of ICH guidelines and Statistical considerations [Chris Wells] (3) Statistical methods for central statistical monitoring [Maciej Fronc]

Harnessing the power of centralized statistical monitoring in post pandemic trial conduct [Tim Rolfe]
The centralised statistical monitoring and quality tolerance limits (CSM/QTL) SIG will present collaborative approaches to centralized monitoring that adapt to study design and data sources, is resilient to environmental disruptions and uses a combination of statistical monitoring techniques and visualizations to drive an efficient monitoring strategy resulting in tangible benefits to quality and resources.  The COVID-19 pandemic caused many challenges for clinical trials. On-site source data verification (SDV) in multicenter clinical trials became difficult due to travel bans and social distancing resulting in a fundamental shift from the traditional on-site monitoring paradigm to one more inclusive of CSM.  Commonly used on-site monitoring techniques are not optimal in finding data fabrication, tampering, non-random data distributions, scientific incompatibility between key measures of interest etc. with the greatest potential for jeopardizing the validity of study results. Quality tolerance limits (ICH E6 R2) are used to proactively control systematic risk to factors critical to quality.  QTLs combined with statistical monitoring techniques reduce spending on inefficient on-site monitoring practices, resulting in diverting resources to increase sample size or conduct more trials.  Complementary to Quality by Design (QbD) principles, this session will provide the framework for risk assessment and identification of relevant data and information deemed critical for quality tolerance limits and centralized statistical monitoring. Examples of studies using CSM and QTLs will be shared, with recommendations on how to best harness the power of statistical monitoring tools in post pandemic trial conduct to better manage risks and achieve targeted actions.

Quality Tolerance Limits: Industry Status, Challenges, Impact of ICH guidelines and Statistical considerations [Chris Wells]
ICH E6(R2) and ICH E8(R1) introduced the concept of a Risk Based Quality Management and Quality by Design approach to Clinical Trials. Quality Tolerance Limits were introduced to the industry to help ensure the quality status of the key risk parameters. In 2023 we are expecting the release of ICH E6(R3) where it is expected for QTLs to no longer be featured but that studies should be setting acceptable ranges. Where are we with QTLs in Industry?  What are the challenges of implementation? What is the impact of ICH E6(R3) and their reference to Acceptable Ranges instead of Quality Tolerance Limits? What are the statistical considerations that need to be taken into account when utilising QTLs and will this change if Acceptable Ranges are set? Should Statistical methodology always be in place? This section of the presentation will discuss the questions

Statistical methods for central statistical monitoring [Maciej Fronc]
Data-driven decisions can be suboptimal when the data are distorted by any kind of inconsistencies. The human eye alone is not great at spotting data anomalies within the great volumes of data captured for a clinical trial. However, they are not intangible at all. Data inconsistencies tend to leave some clues, which might be caught by using quantitative methods. Data inconsistencies affect directly the quality of data. Therefore, there is a need of looking after the data quality by using statistical tools and visualisations. This kind of solutions helps to disclose hidden patterns among data as a basis for decision-making on clinical trial coordination. Monitoring of clinical trials through the prism of central statistical monitoring increases the value that trials generate towards stakeholders. However, the statistical methodology in this field is not yet fully developed. The research on this topic aims to obtain a refined toolbox that improves the centralised monitoring.