What is Source Data Verification or SDV?

In clinical trials, source data verification, or SDV, is the process of validating source data, which is defined by the ICH GCP as “all information in original records and certified copies of original records of clinical findings, observations, or other activities in a clinical trial necessary for the reconstruction and evaluation of the trial.” Simply put, it is the original data upon which a trial is approved and implemented, as well as the data used in making conclusions and assessments - this can include, but is not limited to:

  • Study protocols and documentation
  • Communications and promotional material
  • Forms collected from patients (during on-boarding, from study visits, questionnaires, etc.)

Why is source data verification (SDV) important?

Source data verification acts as an important quality control step to ensure the integrity and accuracy of clinical trial data, and also forms part of the regulatory requirements for clinical trials.

SDV ensures that all data (about patients, study protocols, etc.) has been entered accurately into any electronic systems/records being used, or that physical copies are organized and stored in a clearly identified place. This facilitates later recall, use, and auditing of this data. SDV can involve, for example, comparing a patient's original paper chart to the electronic version of the chart as it was entered manually. If there were any discrepancies found between documents, or if necessary documents were missing, such issues would be resolved during SDV before continuing with the trial.

It is generally accepted that SDV can improve the quality of clinical trials by ensuring data integrity and therefore leading to more robust results. In fact, although it can be a time-consuming and costly process, SDV can often reduce the overall cost of a clinical trial by reducing errors and associated setbacks or problems.

There are other purposes of SDV besides error-checking source data. For instance, SDV can help verify that protocols have been followed correctly, identify trends or patterns within a dataset, and guarantee consistency in data collected across different trial sites, which in turn improves trust between the various researchers who may be involved in a given trial.

For these reasons, SDV is often used as part of a larger quality control process known as "data cleansing." Data cleansing involves comparing datasets with one another so as to identify inconsistencies between them, which could indicate errors or omissions made by researchers when inputting data, corrupt file systems, or errors in software code, for example.

What steps are involved in SDV?

SDV can involve numerous steps, or it could be that only specific steps from this list are performed, depending on the suspected or known quality of the data and how important data verification may or may not be for the specific study.

  1. Data audit - An independent third party audits all the data that has been gathered during a clinical trial. This helps identify any errors or inconsistencies in the data, which can then be corrected.
  2. Data validation - Verifies that all data collected about participants during the trial has been entered correctly into the system and that there are no missing or incomplete entries.
  3. Data quality management - Checks for missing, inconsistent, or incomplete entries in an electronic database by comparing them against other sources such as patient medical records or other databases like electronic health records.
  4. Missing data management - Check specifically for missing entries where data is expected/required, which is especially important when dealing with large populations of participants. It is important that the same fields of information are collected for all patients, for example, to allow conclusions to be drawn based on different classifications according to any number of possible demographic or health factors.
  5. Regulatory compliance - The aim of this step is to ensure that clinical trial data is compliant with all applicable regulatory and legal requirements.
  6. Data Quality Assurance (DQA) - Helps maintain the integrity of data by ensuring that it's properly organized and easy to read.

What are the benefits of using SDV in clinical trials?

There are several potential benefits of using SDV in clinical trials, some of which we have already touched upon:

  1. Improved data accuracy - SDV helps ensure that data collected from patients in the trial is accurate and complete. For example, if a key piece of information in a patient's medical history were to be missed when they are enrolled, it could cause them to be grouped incorrectly and lead to inaccurate conclusions regarding treatment outcomes.
  2. Consistent data - Improves consistency by verifying that data collected by different researchers or at different study sites is similar and similarly organized.
  3. Validated conclusions - SDV can help to guarantee that results and conclusions drawn from a trial are sound as they were arrived at using healthy data.
  4. Compliance with protocol - SDV can be used to ensure and act as confirmation that the study activities and data collection were carried out in line with protocol.
  5. Proof of regulatory compliance - SDV can help with proving regulatory compliance by assuring data quality and providing electronic records of adherence to regulations and legal frameworks.
  6. Reduced staff burden - Depending on the tools used, SDV can potentially reduce staff burden related to correction of manual data entry errors and data verification.
  7. Reduced errors - When there may be problems with data quality, SDV can help catch and correct data input errors, lost data, and incorrect or missing data.

Drawbacks of SDV: Why SDV is not always necessary

Despite the potential benefits, there are also major criticisms of SDV, with critics primarily arguing that it is often unnecessary, in particular in studies wherein there is already a high degree of certainty about data validity and accuracy, or where there is not an explicitly important connection between the data being verified and the trial’s research questions/conclusions or regulatory compliance. In those cases, the time and resources required to perform SDV might be better spent on other types of clinical trial monitoring.

Thus, as always, it is important to understand the specific design and needs of each trial in order to follow the most appropriate steps and allocate resources efficiently, weighing potential benefits of any additional checks against their actual necessity and the use of limited resources.

Conclusions

Source data verification (SDV) is a process employed in clinical trials to check all source data for accuracy and completeness and to ensure it is organized and locatable. The objective of SDV is to confirm that only authentic and reliable source data is used in the clinical trial, and to assist with regulatory compliance and ensure validity of the results. SDV can reduce errors generated at any point during data management, and thereby reduce the risk of any issues with the integrity and legitimacy of the study results.

The potential benefits of performing SDV for each unique trial should be weighed against the time and resources it requires in order to avoid redundancy (i.e., triple-checking data using three different systems may be overkill in many cases) or inefficient allocation of limited resources. There are other trial management and monitoring tools and methods which also include components of data verification, and sponsors and researchers will benefit from identifying and utilizing the tools that most appropriately match the needs of the trial at hand.