Clinical Trial Basics: Source Data Verification (SDV)

SDV Clinical Trial
SDV Clinical Trial

Source Data Verification (SDV) is a method that clinical research organizations (CROs) can use to ensure the data they’re collecting is accurate. It involves comparing a study’s source data against its final report, allowing for the identification of any errors before they become problematic or get published.

What is SDV?

SDV is a process used to verify source data in clinical trials.

Source data is the raw material that makes up a study, which can include:

  • Clinical trial documents and protocols
  • Investigator brochures and other promotional material
  • Study forms, questionnaires, and other participant documents

What’s the purpose of SDV?

SDV helps you verify the accuracy of your clinical trial data. In general, SDV is done prior to starting the study, though it also can be performed at any time throughout the clinical. The purpose of SDV is to:

  • Ensure that all source documents have been collected and are available for review
  • Verify that each record in a source document has been accurately transcribed onto your electronic case report form (eCRF) or your patient registry system
  • Determine if there are any discrepancies between individual sources of information (e.g., patient medical records versus pharmacy dispense records), and resolve them as needed

What are the benefits of SDV?

Source data verification is an essential tool for ensuring the quality of your clinical trial data. By verifying the source documents, you can uncover flaws and mistakes that might otherwise go unnoticed until after your reports are submitted. Some other benefits of SDV include:

  • Ensuring Data Reliability – Verifying the source documents provides a more thorough understanding of how each piece of information was collected and recorded, which provides more confidence in the reliability of those results. This helps to prevent future mistakes from creeping into your study design or analysis plan by revealing potential problems with inputting incorrect patient identifiers or inaccurate timing protocols.
  • Ensuring Data Accuracy – Because SDV involves examining original documents, it’s possible to catch errors that may have been missed during manual transcription processes (e.g., if there are inconsistent formats between different types of text fields).
  • Ensuring Consistency – With many studies involving multiple sites across several regions, states, or countries, it can be difficult for one person to keep track without some form of checking system in place—which is precisely what SDV offers. It ensures everything is checked to ensure it all matches up correctly before submission, reducing the odds discrepancies slip through undetected. This also helps build trust between investigators.

What are the limitations of SDV?

In clinical trials, SDV helps ensure that the data entered is accurate and valid. It is time-consuming and expensive, but it’s only as good as the data you enter into it. If there are errors in your source documents, then those errors will be replicated when you submit them for SDV.

Fortunately for all parties involved, not all studies require source data verification. For instance, if a study has already been completed successfully or if there are no regulatory requirements related to your clinical trial, then SDV may not be necessary.

What are some alternatives to SDV?

If you’re thinking of doing SDV, there are a few things to keep in mind. First and foremost, it is the most reliable way to make sure that your data is accurate and reliable. Second, if your company doesn’t already have an SDV program in place, it can be a good investment. Thirdly, if you think that this kind of verification isn’t necessary for your trial or study, that isn’t typically the case.

Still, that doesn’t mean that SDV is the only option. In some cases, trial oversight committees may be a functional alternative. Risk-based monitoring is also a potential solution. You may also want to explore hybrid approaches to clinical trial monitoring, as these can potentially allow you to capture benefits from different methodologies or avoid specific drawbacks associated with various strategies.

Using SDV in your clinical trials can help uncover mistakes and make sure your data is reliable.

Source Data Verification is a critical step in the clinical trial process. SDV is used to confirm that the source data is accurate and complete as it is collected, stored, shared, and analyzed. It helps ensure that your data will be reliable and can be depended upon for decision-making during your clinical trials.

Conclusion

SDV can help you build a better clinical trial and get the results you need. It’s important to know that SDV is only one of many tools in your toolbox, though. If you’re not sure what kind of data verification strategy will work best for your project, explore all of the available options, allowing you to select the ideal path while ensuring regulatory compliance.