Clinical Data Validation

Disambiguation: Clinical data validation means checking clinical data for correctness and completeness. Clinical database validation is making sure that clinical databases perform the functions required by regulations and guidances and is on another page.

• More information on the disambiguation.
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What is Clinical Data Validation?

Data validation is a series of documented tests of the data with the goal of ensuring the quality and integrity of the data.

More specifically, validation is usually concerned with checking four of the eight characteristics of good clinical data – these characteristics are from the first guidance and the first other reference listed below. The eight characteristics are:

• Attributable: The sources of the data are known and recorded.
• Legible: The data are human readable.
• Contemporaneous: The source data are recorded when they are generated.
• Original: All data come from the original source.
  • Copies and transformations of the data are accurate and complete, do not overwrite original data, and are traceable back to original data.
• Accurate: The data are correct.
• Enduring: The data are available for the entire time they are required to be kept.
• Complete: All available data are included.
• Consistent: All of the data use consistent terms and are non-contradictory.

Data validation tests usually check the original, accurate, complete, and consistent aspects of the data.

Why does Clinical Data need Validation?

From a business perspective, the data are how the FDA, other regulators, and business partners evaluate the worth of the product. From an ethical perspective, clinical data affect treatment decisions, which affect patient health, and the patient population in question is virtually all of the United States and a significant fraction of the rest of the world. For both of these reasons, clinical data quality and integrity are critical.

Despite this, few regulations talk about data validation directly. Instead, the regulations and guidances focus on requirements that the data handling systems must meet to ensure data quality and integrity. Regulations and guidances that do mention clinical data validation, or a part of the process, are listed below.

What is the Validation Process?

The general outline for data validation is listed below. However, the validation process is complex and dependent on the data captured, business and regulatory concerns, the data management software used, and several other factors, so there are many possible variations and options.

• Planning
  • The Sponsor decides what checks should be used, what code lists are appropriate, and what procedures will be used for invalid results.
  • The checks, code lists, and procedures are documented.
• Implementation and Testing
  • The checks and code lists are implemented in the clinical database management system.
  • Test procedures and test data for the checks are created, usually as part of database validation.
  • The test procedures are performed.
• Data Entry and Validation
  • The checks are run during data entry, either as the data are entered or at intervals.
  • Invalid results are fixed or allowed using the planned procedures.
  • The final set of checks is usually referred to as data cleaning.
• Database Lock
  • When no more updates or changes to the data are expected the database is locked.

Even after database lock, analysts may run further checks to determine if any changes are necessary in order to produce the analysis datasets.

Regulations and Guidances

• Guidance for Industry Computerized Systems Used in Clinical Investigations – The guidance defines the “fundamental elements of data quality” as “attributable, legible, contemporaneous, original, and accurate”. This includes both paper and electronic records.
• Guidance for Industry E6 Good Clinical Practice: Consolidated Guidance – The International Conference on Harmonisation (ICH) guidance on Good Clinical Practice, which is also an FDA guidance. Section 5.5 has a requirement for data verification. The same section requires controls for data handling systems that imply the need for “completeness, accuracy, [and] reliability” of the data itself.
• Guidance for Industry E9 Statistical Principles for Clinical Trials – The ICH guidance on clinical trial statistics, which is also an FDA guidance. Section 5.5 calls for specific attention to missing values and outliers. Section 5.8 requires controls for data integrity.

Other References

• Computerized Systems in Clinical Research: Current Data Quality and Data Integrity Concepts – A general guide to clinical data system quality and integrity.