Primer on Clinical Trial Data Validation Process, Part 1

September 08, 2020
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[vc_row][vc_column][vc_column_text]Within any clinical trial or research study for a given condition, treatment or drug, the quality and integrity of the data involved are vital factors. There are several tests or programs researchers will run to ensure their data is of the highest quality possible, and this broad process is known as clinical data validation, or process validation.

At AGA Clinical Trials, we’re happy to explain the clinical data validation process we observe for any of our clinical research studies to help study participants or others understand how we ensure only the best data possible. Within this two-part blog series, we’ll go over everything you need to know about the clinical data validation process, from why it’s important to how it’s carried out and some of the guidance systems that govern it.

clinical trial data validation

Clinical Data Validation Basics

First and foremost, a couple simple definitions that people often confuse will help us define clinical data validation. This term refers to ensuring correctness and completeness of all data within a study; clinical database validation, on the other hand, speaks to ensuring clinical databases perform the functions required by regulations. For the purposes of this primer, we’re discussing only the former area, clinical data valuation.

As we noted above, clinical data valuation involves several potential tests that validate the qualities of the data. There are eight characteristics that may be checked for:

  • Attributable: Sources of data are known and recorded.
  • Legible: Data can be read by humans.
  • Original: All data comes from the original source, and any copies or transformations are completely accurate and do not overwrite original data.
  • Contemporaneous: Source data are recorded at the time they’re generated, not later.
  • Accurate: All data are correct.
  • Enduring: Data remain available for the entire time they are needed.
  • Complete: All available data are included.
  • Consistent: All data uses consistent terms and are non-contradictory.

Now, most data validation tests only check the original, accurate, complete and consistent aspects here. However, some will check for all eight.

Why Validation is Necessary

Clinical data validation is vital for more than one reason. It’s important from a business angle, allowing not only the FDA but also regulators, business partners and others in the space to evaluate a product accurately and make judgements on it.

However, data validation is also vital for simple ethical reasons. Clinical data often has a direct impact on patient treatment decisions and patient health, and for this reason it’s of paramount importance that such data be completely accurate and robust at all times. While more attention is often paid to data handling systems, it’s vital to validate data first and foremost.

For more on clinical data validation within medical research trials, or to learn about any of our clinical studies, speak to the staff at AGA Clinical Trials today.[/vc_column_text][/vc_column][/vc_row]

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