In our most recent blog post, we tackled the topic of building a study and discussed why it’s important to make the process as quick and cost-effective as possible. With the right electronic data capture (EDC) platform, designing case report forms (CRFs) and configuring study settings should be easy. Plus, it shouldn’t break the bank. Within this post, we’ll take a look at the step that’s successive to study building: validation. Read on to learn all about validating clinical research studies.
What is validation?
This is an important step in preparing a clinical research study to go live. And, as a bigger picture, validation helps to ensure data quality and reliability for life-improving treatments. Validation is the process of testing the behavior of the study’s data points, edit checks, and workflow – with the goal of surfacing any unintended design flaws. It ensures the source data collected leads to qualified data for applying toward accurate treatment outcomes.
The traditional way to validate
An older, more cumbersome method of validation on electronic platforms is to use spreadsheets to keep track of how a study was built. Research teams detail each element of the study’s data points and then turn to the EDC system to make sure the function is aligning with the design. They return again to the spreadsheet to manually log results. For instance, they will go through the motions of completing data entry on a CRF and test that edit checks fire correctly. Any edit checks that do not work as intended are logged on the spreadsheet, modified, and re-tested. This process will ultimately serve as a test script that comes in handy when an FDA-submitted study goes through an audit.
Leveraging technology to validate
The traditional method of validation doesn’t have to be the only way. eClinical software exists to improve your processes, not add more steps to complete. TrialKit has a number of features in place to make validation more efficient and accurate:
Auto-validation tool
TrialKit has a unique auto-validation feature that reduces the burden of manually validating each CRF in a study. Essentially, the system emulates every action made by a user when completing a form. The test data run through the form is created automatically; then, the test scripts that are automatically generated are editable by the study designer. Now, instead of having to manually complete a spreadsheet, the system will simply create a log of all of the edit checks that were fired and the corresponding test data. By evaluating this log, research teams can make the appropriate fixes and spend a fraction of the time doing so. It also saves great amounts of time on repeated data entry. Every validation is stored in the system with a time stamp, so they can be referenced in the future.
Data dictionary
Within TrialKit’s form builder, the data dictionary for every form is made available. This function creates a table, or blueprint, that details every element on each dataset (CRF), such as the field types used, variable names, coded choices, and programmed behavioral logic – including edit checks. The data dictionary can be exported in Excel or ODM for importing on any other ODM-supported platform.
Native mobile app
Employing the use of a mobile app helps accelerate the validation process. TrialKit’s app becomes a playground when testing CRFs and study design. Because the app processes the changes and immediately uploads them to the database, research teams are able to make necessary revisions quicker than ever. Working simultaneously in a web browser and mobile app eliminates the need for multiple windows to bounce between the form builder and the testing environment. Study builders can modify forms on the web form builder and test them in parallel via the app to try them out. Changes are instantaneously reflected.
What about protocol changes?
Validation isn’t a one-and-done step in clinical data management. A study will experience protocol changes, triggering the need to re-validate the changes made. According to a study conducted by Tufts Center for the Study of Drug Development, it takes research teams an average of 68 days to build and release a study database. 45 percent of the study respondents cited protocol changes as the source of delay. While being met with protocol changes is inevitable, making those changes in the database should not contribute to study delays.
TrialKit employs true version control to make it much easier to validate protocol changes on a study: the system logs those changes for the user through built-in versioning. This helps maintain Good Clinical Practice (GCP) by generating easily accessible, accurate documentation in the event that it is needed for an audit. Those changes can even be tested on the fly while the current study version continues operation as normal. Once the protocol is live, subjects can be migrated over to the newer validated version.
Do you feel like your validation process could be streamlined to become quicker and less expensive? Request a demo today to have one of our product experts show you how it all works in TrialKit.
