Your Easy Guide to Clinical Studies

Laboratories or other analytical systems generate study data. The ability to import data from these systems into the study database (eCRF) can:

• Simplify the data collection and documentation process
• Avoid human transcription errors

 

Still, as different software systems utilise different means to document and store data, the transfer between different software systems may require significant reformatting efforts.

 

Thus, the translation of information from one system source to another must be defined prior to any data import. Different software systems commonly have different standards regarding data structure. While the information may be intuitively obvious to a human reader, the same does not always hold true for a computer system.

 

Required adaptations may include:

• Data formatting (e.g. male gender is coded differently and must be harmonised)
• Structural and technical information (e.g. variable names, visit names and ID, repetitions in line or in columns)

More

As a SP-INV and in collaboration with the Data Manager (DMan) of your study:

• Clarify whether data can be exported from a primary system source of interest
• Collect information on the set-up and structure of the data to be exported (e.g. formatting-, metadata information)
• Define with the DMan how:
  • Data must be reformatted in order to be compatible with the study database (eCRF)
  • To validate any reformatted data, based on the requirements of your study database 
• Perform data import only once the data you plan to import has been appropriately adapted from the primary source

 

Data manipulation processes:

• Are time-consuming and have a high error risk if done incorrectly (e.g. incorrect reformatting, the skipping of format validation prior to merging, faulty implemented data merging procedures)
• Must be documented and reproducible

 

As these processes can be quiet challenging, it might be advisable to outsource some of these services.

References

ICH GCP E6(R2) – see in particular guidelines

• 5.5. Trial Management, data handling, and record-keeping