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Improving Risk Assessment in Clinical Trials: Toward a Systematic Risk-Based Monitoring Approach
(2021)
Regulatory authorities have encouraged the usage of a risk-based monitoring (RBM) system in clinical trials before trial initiation for detection of potential risks and inclusion of a mitigation plan in the monitoring strategy. Several RBM tools were developed after the International Council for Harmonization gave sponsors the flexibility to initiate an approach to enhance quality management in a clinical trial. However, various studies have demonstrated the need for improvement of the available RBM tools as each does not provide a comprehensive overview of the characteristics, focus, and application. This research lays out a rationale for a risk methodology assessment (RMA) within the RBM system. The core purpose of RMA is to deliver a scientifically based evaluation and decision of any potential risk in a clinical trial. Thereby, a monitoring plan can be developed to elude prior identified risk outcome. To demonstrate RMA’s theoretical approach in practice, a Shiny web application (R Foundation for Statistical Computing) was designed to describe the assessment process of risk analysis and visualization tools that eventually aid in focusing monitoring activities. RMA focuses on the identification of an individual risk and visualizes its weight on the trial. The scoring algorithm of the presented approach computes the assessment of the individual risk in a radar plot and computes the overall score of the trial. Moreover, RMA’s novelty lies in its ability to decrease biased decision making during risk assessment by categorizing risk influence and detectability; a characteristic pivotal to serve RBM in assessing risks, and in contributing to a better understanding in the monitoring technique necessary for developing a functional monitoring plan. Future research should focus on validating the power of RMAs to demonstrate its efficiency. This would facilitate the process of characterizing the strengths and weaknesses of RMA in practice.
Monitoring of clinical trials is a fundamental process required by regulatory agencies. It assures the compliance of a center to the required regulations and the trial protocol. Traditionally, monitoring teams relied on extensive on-site visits and source data verification. However, this is costly, and the outcome is limited. Thus, central statistical monitoring (CSM) is an additional approach recently embraced by the International Council for Harmonisation (ICH) to detect problematic or erroneous data by using visualizations and statistical control measures. Existing implementations have been primarily focused on detecting inlier and outlier data. Other approaches include principal component analysis and distribution of the data. Here we focus on the utilization of comparisons of centers to the Grand mean for different model types and assumptions for common data types, such as binomial, ordinal, and continuous response variables. We implement the usage of multiple comparisons of single centers to the Grand mean of all centers. This approach is also available for various non-normal data types that are abundant in clinical trials. Further, using confidence intervals, an assessment of equivalence to the Grand mean can be applied. In a Monte Carlo simulation study, the applied statistical approaches have been investigated for their ability to control type I error and the assessment of their respective power for balanced and unbalanced designs which are common in registry data and clinical trials. Data from the German Multiple Sclerosis Registry (GMSR) including proportions of missing data, adverse events and disease severity scores were used to verify the results on Real-World-Data (RWD).