What Story Does Your Clinical Trial Data Tell?
A common challenge facing many life sciences companies is in getting a clear understanding of what their clinical trial data can tell them. Life sciences companies have data in various systems, such as electronic data capture (EDC) and clinical trail management systems (CTMS), and pulling the data together and learning from it requires a team of programmers and quite a bit of time.
Advances in technology enable data to be integrated easily into a Clinical Data Repository and offer advanced analytics capabilities that can generate real-time visualizations that empower companies to make impactful business-decisions quickly. The saying, “a picture is worth a 1,000 words” is true.
In order to generate a data visualization that tells a compelling story and helps you meet your goals, ask yourself the following questions:
1. What question(s) are you seeking to answer? What actions will be taken based on the answer(s)?
2. What is the intent of the visualization? Will the visualization be used for monitoring safety data, insights into trial metrics, or risk based monitoring? A clear statement of the intent serves as a guide while developing the visualization and ensures it meets your goals.
3. Who will use the visualization? Determine if the visualization will be used primarily for one role, such as a data manager, clinical scientist, medical monitor, clinical project manager, or for several different roles or teams. If the visualization will be used by a team, consider how the various roles will use the visualization to make decisions and what data should be included to make the visualization valuable.
4. Will it be study specific or support cross-trial analysis? The answer to this question determines how the summary and detail data will be displayed (for example, by visit if study specific or by study day if cross-trial).
5. How frequently is the data refreshed? The frequency that the data is refreshed could help design the the visualization. If its real-time, implementing dashboards with monitoring indicators will be beneficial.
6. What categories will the visualization fall into? Is this a domain-specific (ie, adverse events, exposure, disposition, etc.) visualization or cross-domain (ie, safety, efficacy, etc.) based visualization?
7. Is this a summary visualization or detail data visualization? If it is a summary visualization, is there a need to drill-down from the aggregated summary views to detailed subject level views?
8. Does comparative data need to be shown for meaningful analysis? If so, determine the sources of the comparative data and ensure appropriate charts are implemented for easy visual comparisons.
Developing effective visualizations allows life sciences companies to leverage their clinical data for insights that impact the speed and quality of the decision-making process.
Our team has developed numerous visualizations to help clinical data tell compelling stories. Is your team using visualizations to learn from clinical data? Please share your challenges and insights with us.
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