Data Visualization: A Useful Tool

How to fully exploit data visualization in clinical trials to ensure all valuable insights are unlocked.

Data visualization is a hot topic in clinical trials with its potential to increase efficiency, protect participant safety, and guide informed decision-making to deliver the best chances for trial success. However, data visualization is evolving rapidly and is about far more than just moving from lists and spreadsheets to graphs and charts. Choosing the right analysis approach, determining the specific research focus, and harnessing potential for increased understanding and engagement are all required stages to achieve successful data visualization. Without such essential steps, any potential insights will remain invisible.

The Basics of Data Visualization

Put simply, data visualization is the graphical representation of information and data in a way which aids quick and easy comprehension. It can include the use of graphs, indicators, trend lines, and other visual representations. These types of visualizations allow us to identify features that traditional methods like lists and spreadsheets may miss—for example, unusual data distributions, local patterns, missing values, or outliers.

Data visualization is well-recognized as a useful tool to aid quick and easy interpretation and understanding. However, what is transforming data visualization applications in an era of “big data” is the ability to create visuals in near real-time and to customize dashboards. This can, for example, enable users to seamlessly draw connections between the clinical research interests and the patient records. For example, a medical monitor might previously have needed data listings, source systems, or to request from programmers a complete general data report or patient profiles to perform the medical review duty; however, today’s analytics apps can enhance the medical review process using real-time automatic data analytics to achieve more timely detection of events and signals of special interest. By providing this holistic view of each patient through consolidated patient profiles, backed by intuitive visualizations, it empowers medical monitors to make data-driven decisions, improving the likelihood of successful trial outcomes.

The right tool can significantly enhance data understanding, insight discovery, communication, decision-making, efficiency, collaboration, data quality, compliance, and productivity. So, how can we identify the right tools built with strong domain knowledge?

Domain Knowledge

Any visualization tool is only as good as the domain knowledge behind it. A data visualization tool is incomplete unless it fits seamlessly within existing clinical processes, such as within Data Management, Clinical Operations, Medical Monitoring, or Risk-Based Monitoring. The users should not need to adapt their ways of working or spend redundant effort defining their needs to the developers. The only difference should be that they now have data access at their fingertips and therefore more enhanced data transparency and clarity to deliver their data review responsibilities.

Customizable and Collaborative

Today’s multi-provider, decentralized, digitally enabled environment has led to an increased number and variety of clinical data sources. At the same time, each trial possesses its own unique characteristics, making the implementation of industry standard metrics a complex task. Trial teams frequently request customized visualizations that align precisely with their monitoring plans. In platforms lacking native visualization capabilities, implementing even minor changes to charts and tables can be arduous and time-consuming.

In this environment, it is vital data visualizations are customizable. They must be able to consolidate complex data from multiple sources into protocol-specific dashboards that allow for the rapid monitoring of success-critical metrics. They must also empower trial teams to tailor visualizations to exact specifications within a short timeframe. By increasing access to user-friendly visualizations, we can free up more time to gain deeper data insights and address anomalies while spending less time trying to analyze data in sub-optimal formats.

Collaborative features are also key to modern data visualizations. These enable the sharing of insights across functions, team members, and stakeholders, allowing collaborative review, decision-making, and compliant documentation. Quick data and table export features can also allow users to easily take their data elsewhere when needed, further increasing the chances of increased engagement.

Case Study—Identifying Bottlenecks and Formulating Solutions

Sponsor X had clinical trials which frequently failed to meet the target of administering the first treatment to the last participant within the planned time frame. The problem stemmed primarily from an inability to swiftly identify bottlenecks within their studies and formulate effective solutions.

Intelligent use of data analytics and visualization software provided valuable insights into recruitment efforts, predicted when the last participant would receive treatment, and proposed strategic reallocation scenarios at regional/country/site levels. It factored in non-started sites, improving forecasts for situations where sites opened later in the recruitment phase, and integrated a novel Kaplan-Meier model to predict participant progression based on their screening duration. The system featured real-time data updates, accessible to all Sponsor X personnel, facilitating swift access and customized filtering of forecasts across various geographical levels.

An innovative feature was the integration of a write-back solution, which empowered authorized users to view forecasts and actively reallocate participants between different countries, or sites, based on real-time data and predictions. This reallocation capability allowed Sponsor X to adapt dynamically to recruitment challenges and optimize participant distribution for maximum efficiency.

Benefits of this data analytics and visualization-led approach for Sponsor X included:
• Accelerated trial completion
• Proactive enrolment management
• Data-driven decision making
• Significant cost savings

Integration of the Kaplan-Meier model also enabled different countries to optimize their sites based on participants in screening and their probabilities of progression. This precision reduced timelines by three weeks in the 26-week trial, resulting in significant savings.

Data visualization is becoming a fundamental tool in modern clinical trial conduct. Advanced, customizable visualizations aid decision making and collaborative review across the clinical trial value chain. In addition, the automation data feeding components in data visualization allows users to review data and analytics at any time, without the need to request data processing and reporting from colleagues with programming skillsets. Thus, data visualization largely streamlines workflow and significantly empowers all users. Specific functionalities such as quality tolerance limit review and blinded placebo effect monitoring can also be built into data visualization, to facilitate early detection of systematic bias in clinical trials.

By ensuring sponsors extract the maximum value from their data and minimize manual process, they optimize the speed and efficiency of data analysis strategies, while improving reaction time and reducing costs. When perfected, visualizations ensure that all important data never escapes attention.