Understanding the Various Considerations for EDC Selection
“Sponsors must think about their Electronic Data Capture (EDC) system from the very beginning, taking the entire clinical program into consideration rather than just a single study. They should balance their short-term goals with their long-term vision for the program.”
When should a sponsor begin thinking about EDC selection? Why?
This should be done from the get-go, but in reality, most sponsors start when their protocol is nearly finalized which is sometimes later than we would like. Sponsors really should consider the program as a whole upfront when selecting the right EDC solution, rather than making a decision based on just that individual study. If the trial is successful and the program grows into a larger, next-phased study, will they be able to scale up in that EDC system? Does it have the functionality to support a larger project with more sites, subjects, users, and needs? More often than not, it is most advantageous for sponsors to be able to continue working within the same EDC system for the lifecycle of a program and even cross-programmatically, where they have already developed standards, edit checks, and gained efficiencies, instead of moving to a different one. This strategy of staying within a chosen EDC system saves time and ultimately money for sponsors. They already know the forms work well for what they need and do not need to reinvent the wheel each time.
What are the key selection criteria for picking the right EDC system?
Every stakeholder in a clinical trial will have different priorities and wish lists for what they want within EDC, but it is important to assess all the options and strike a balance between everyone’s needs and wants.
Clinical Operations stakeholders often wonder:
- What is the functionality for the end-user?
- How easy will it be for them to log in, understand the tasks, enter data and resolve queries? Is there any brand recognition?
- Are the end-users already familiar and trained in that software? If so, they will need less training if they have used that EDC before.
- Are there dynamic functions that can hide fields that are not applicable to streamline data entry?
Oftentimes, that kind of functionality is a top priority for clinically-minded folks, while those on back-end services like Biostats and Data Management focus more on how they will get the data out of the system and in what format. Their concerns center around:
- Will the data be Study Data Tabulation Model (SDTM) compliant?
- Are there additional modules built into the system, like randomization or drug dispensing? How much time will it take to build the database?
- Will someone in-house do the build or will it be contracted out to a CRO?
- What is the overall cost of the system?
- What are the reporting needs?
- Does it have a dashboard to view the data?
- Is there a need to integrate other data into EDC or for it to talk to another system like a central lab or CTMS?
What are some of the misconceptions sponsors need to be aware of?
Some EDC vendors may claim their clinical database can be built really quickly, but that is somewhat of a misconception. Yes, some systems are more user-friendly for database builders, but the study build timeline is actually dictated by processes and not by systems. Different CROs and sponsors have different processes for building Case Report Forms, programming edit checks, User Acceptance Testing, and putting all that content into documents for approval. Often times the process around those steps is the real gatekeeper for how long a build takes.
There are also incorrect notions around the cost of some systems. Some are perceived as more expensive or cheaper, but they all have different algorithms to calculate costs that include the overall duration and size of the study. No system is universally cheaper. What may be considered expensive for an individual study could in reality be the most cost-effective solution in the long-term if the sponsor will continue to develop a program within that system, capitalizing on efficiencies from previous builds. What may be most expensive for a phase I study may be significantly less expensive than a competitor for the phase II/III study if the program advances.
Is it cost-effective for virtual/emerging biotechs to use EDC for their trials?
Yes, it is. The days of paper data collection are gone, and everyone has moved to EDC. EDC is generally significantly more cost-effective, namely because most of the infrastructure for paper studies has been dissolved to make space for EDC and the shifting responsibility of data entry to the site personnel. It takes less time for sites to enter data and we have seen significant improvements in the accuracy and timeliness to achieve complete data entry when the site enters it, rather than sending Case Report Forms and queries through the mail. In this industry, we know time is money.
Is it easier to use a third party to build a database in EDC, or to do it in-house?
It depends. If you are considering doing it in-house, who will actually do the build? A sponsor with employees who have some familiarity or experience in building relational databases may potentially have the capability in-house, provided they have the time to devote to it. Most builds are more time-intensive than you might think off the bat. Some EDC systems are easier to learn than others – there are some easy “drag and drop” EDCs that are quick to learn but may not have as much powerful functionality that you might want or need.
In reality, many folks do not have the time or energy to commit to learning the system, passing the certification requirements to build, and devoting to build a full database. It is usually better to have people who are experienced at building a database, who know the common pitfalls and can work nimbly and quickly applying those lessons learned.
- View the infographic on Database Design Considerations
- Read the case study on the rapid deployment of a Covid-19 trial database.
- Explore our Data Management expertise
- Register for our upcoming Dec 10 webinar on "Achieving Balance in Clinical Trial Database Design: Considerations for Enhancing Clinical Trial Efficiency"