What is it? Why is it important?

Laboratories or other analytical systems generate study data. The ability to import data from these systems into the study database 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 entail significant reformatting efforts.

Thus, the translation of information from one system source to another must be defined prior to any data transfer.

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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. where male gender is coded differently and must be harmonised)
  • Structural and technical information (e.g. variable names, column names, dataset location)

Any adaptations should be done by the DMan in collaboration with the study team before data transfer, as only correctly formatted data guarantees successful data import into the database (eCRF) of the study.

What do I need to do?

  • 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 of yours study how
    • Data must be reformatted in order to be compatible with the study database
    • To validate any reformatted data based on the requirements of your study database 
  • Perform any data import only once the data has been appropriately adapted in the primary source

More

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.

Where can I get help?

Your local CTU can support you with experienced staff regarding this topic

References

ICH GCP E6(R2) – see in particular guidelines

  • 5.5. Trial Management, data handling, and record-keeping
Abbreviations
  • CTU – Clinical Trials Unit
  • DMan – Data Manager
  • eCRF – Electronic Case Report Form
Conduct ↦ Data Handling ↦ Data Handling ↦ Data Import
Study
Basic

Provides some background knowledge and basic definitions

Basic Protocol
Basic Statistics
Basic Monitoring
Basic Drug or Device
Basic Biobanking
Concept

Starts with a study idea

Ends after having assessed and evaluated study feasibility

Concept Protocol
Concept Statistics
Concept Drug or Device
Concept Biobanking
Development

Starts with confidence that the study is feasible

Ends after having received ethics and regulatory approval

Development Protocol
Development Statistics
Development Drug or Device
Development Biobanking
Set-Up

Starts with ethics and regulatory approval

Ends after successful study initiation

Set-Up Protocol
Set-Up Ethics and Laws
Set-Up Statistics
Set-Up Drug or Device
Set-Up Biobanking
Conduct

Starts with participant recruitment

Ends after the last participant has completed the last study visit

Conduct Protocol
Conduct Statistics
Conduct Drug or Device
Conduct Biobanking
Completion

Starts with last study visit completed

Ends after study publication and archiving

Completion Protocol
Completion Statistics
Completion Drug or Device
Completion Biobanking
Current Path (click to copy): Conduct ↦ Data Handling ↦ Data Handling ↦ Data Import

Please note: the Easy-GCS tool is currently under construction.