Good Statistical Monitoring: A Flexible Open-Source Tool to Detect Risks in Clinical Trials.

BACKGROUND: Risk-based quality management is a regulatory-recommended approach to manage risk in a clinical trial. A key element of this strategy is to conduct risk-based monitoring to detect potential risks to critical data and processes earlier. However, there are limited publicly available tools to perform the analytics required for this purpose. Good Statistical Monitoring is a new open-source solution developed to help address this need. METHODS: A team of statisticians, data scientists, clinicians, data managers, clinical operations, regulatory, and quality compliance staff collaborated to design Good Statistical Monitoring, an R package, to flexibly and efficiently implement end-to-end analyses of key risks. The package currently supports the mapping of clinical trial data from a variety of formats, evaluation of 12 key risk indicators, interactive visualization of analysis results, and creation of standardized reports. RESULTS: The Good Statistical Monitoring package is freely available on GitHub and empowers clinical study teams to proactively monitor key risks. It employs a modular workflow to perform risk assessments that can be customized by replacing any workflow component with a study-specific alternative. Results can be exported to other clinical systems or can be viewed as an interactive report to facilitate follow-up risk mitigation. Rigorous testing and qualification are performed as part of each release to ensure package quality. CONCLUSIONS: Good Statistical Monitoring is an open-source solution designed to enable clinical study teams to implement statistical monitoring of critical risks, as part of a comprehensive risk-based quality management strategy.

Assessing the performance of methods for central statistical monitoring of a binary or continuous outcome in multi-center trials: A simulation study.

BACKGROUND: Quality study monitoring is fundamental to patient safety and data integrity. Regulators and industry consortia have increasingly advocated for risk-based monitoring (RBM) and central statistical monitoring (CSM) for more effective and efficient monitoring. Assessing which statistical methods underpin these approaches can best identify unusual data patterns in multi-center clinical trials that may be driven by potential systematic errors is important. METHODS: We assessed various CSM techniques, including cross-tests, fixed-effects, mixed-effects, and finite mixture models, across scenarios with different sample sizes, contamination rates, and overdispersion via simulation. Our evaluation utilized threshold-independent metrics such as the area under the curve (AUC) and average precision (AP), offering a fuller picture of CSM performance. RESULTS: All CSM methods showed consistent characteristics across center sizes or overdispersion. The adaptive finite mixture model outperformed others in AUC and AP, especially at 30% contamination, upholding high specificity unless converging to a single-component model due to low contamination or deviation. The mixed-effects model performed well at lower contamination rates. However, it became conservative in specificity and exhibited declined performance for binary outcomes under high deviation. Cross-tests and fixed-effects methods underperformed, especially when deviation increased. CONCLUSION: Our evaluation explored the merits and drawbacks of multiple CSM methods, and found that relying on sensitivity and specificity alone is likely insufficient to fully measure predictive performance. The finite mixture method demonstrated more consistent performance across scenarios by mitigating the influence of outliers. In practice, considering the study-specific costs of false positives/negatives with available resources for monitoring is important.

The Safety Explorer Suite: Interactive Safety Monitoring for Clinical Trials.

BACKGROUND: Frequent and thorough monitoring of patient safety is a requirement of clinical trials research. Safety data are traditionally reported in a tabular or listing format, which often translates into many pages of static displays. This poses the risk that clinically relevant signals will be obscured by the sheer volume of data reported. Interactive graphics enable the delivery of the vast scope of information found in traditional reports, but allow the user to interact with the charts in real time, focusing on signals of interest. METHODS: Clinical research staff, including biostatisticians, project managers, and a medical monitor, were consulted to guide the development of a set of interactive data visualizations that enable key safety assessments for participants. The resulting "Safety Explorer" is a set of 6 interactive, web-based, open source tools designed to address the shortcomings of traditional, static reports for safety monitoring. RESULTS: The Safety Explorer is freely available on GitHub as individual JavaScript libraries: Adverse Event Explorer, Adverse Event Timelines, Safety Histogram, Safety Outlier Explorer, Safety Results Over Time, and Safety Shift Plot; or in a single combined framework: Safety Explorer Suite. The suite can also be utilized through its R interface, the safetyexploreR package. CONCLUSIONS: The Safety Explorer provides interactive charts that contain the same information available in standard displays, but the interactive interface allows for improved exploration of patterns and comparisons. Medical Monitors, Safety Review Boards, and Project Teams can use these tools to effectively track and analyze key safety variables and study endpoints.