Data visualization in Clinical Trials

 

"Data plays a key function in organizing, monitoring, and analyzing findings for a clinical study, similar to the foundation of a new house. Data provides new insights, helps evaluate hazards, and helps choose the best course of action for a study.

Introduction

In clinical trials, adverse events are often reported by simply counting the number of people who experienced each event. Reporting only frequency leaves out other data aspects that are crucial for stakeholders, such as severity, seriousness, rate (recurrence), timing, and groups of connected Adverse events. Data visualization is the process of displaying data in such a way that it can be easily understood. It helps to identify patterns and trends and make decisions based on this information. A good data visualization tool will allow you to quickly summarize your data and make it easy for others to understand what it means.

In the last ten years, there has been an increase of 183% in data per clinical trial because of breakthroughs in science and technology. The amount and variety of such data have grown well beyond what a straightforward spreadsheet can handle. The volume and variety of data generated by clinical trials will increase as they adopt decentralized methods. The main obstacles for sponsors of all sizes are importing and evaluating data from wearables, imaging systems, apps, and central labs. The importance of curating and delivering the data to stakeholders is growing along with the difficulty and time commitment. To complete these responsibilities, a platform that enables research teams to access all data in one location is now essential.

What is Data visualization?

Data visualization is the graphical representation of information and data using visual elements like charts, graphs, maps, and other visuals. It uses a variety of techniques, such as graphs and charts, pie charts and bar graphs, line graphs (to compare two sets), maps, timelines (to show repeated observations), histograms (to find outliers), box plots, etc.,

Comparison of traditional frequency tables and data visualization

Data visualization is more effective than frequency tables because it allows you to compare data more intuitively. A traditional frequency table shows how many times each option was selected but does not allow you to see any other information about the response (e.g., mean, or median). This can be time-consuming if multiple options are being compared or if many different metrics are being displayed on one page with no space between them

Power of Data Visualization

Traditional methods just don't allow for efficient use and administration of that data when the volume of data increases and decentralized trials become more prevalent. You may easily integrate, organize, and analyze clinical data using visualization tools to boost operational effectiveness and drive clinical trial success. In addition to the obvious benefit of being able to identify outliers and trends, data visualization also helps with identifying clusters, correlations, and relationships. These are all ways that you can use your data to inform decisions about a trial.

Benefits of data visualization

• More informed decisions

• Faster analysis 

• Improved organizational efficiency 

• Visualizations can better support investigators to assimilate large volumes of data and enable improved informal between-arm comparisons compared to tables

• Increased competitive advantage

• Improved customer experience

• Visualization can show data quality issues, support robust temporal searches, or even discover cohorts of patients meeting selection criteria for clinical studies that depend on huge warehouses of patient data.

• The availability of more internet information and personal sensors has begun to raise patient awareness of and ownership over their health. This is a significant departure from the paternalistic approach to healthcare in which patients trust their doctors with their health during annual checkups or in the event of an injury or illness.

• Provide insights into the relationships between variables and help you identify potential flaws in your study design.

Conclusion

Data visualization is a powerful way to improve the quality of Clinical trial data. The use of tables, dot plots, and volcano plots can encourage differing interpretations. This can be achieved by providing interactive tools for data exploration and analysis, as well as visual displays that are easy to interpret and understand. Care in the construction of visual displays needs to be taken as there can be potential to overemphasize treatment effects in some circumstances. 

Dr. Maitreyee J. Kane

BDS

Student ID: 196/1122