d. Monitoring and validation

You can protect the scientific integrity of your study by consistently monitoring the data entry process, i.e., documenting who enters or modifies a particular data element at what location and time. This is mandatory for clinical trials. In addition, it is advisable to implement a method for validating the data after initial entry.

You should preferably store data entry information within the software that you are using. Many software packages do this automatically in the so-called audit trail. An audit trail is mandatory for clinical trials.

In addition, it is advisable to implement a method for validating and cleaning the data after initial entry and to decide when a dataset will be locked for the start of analysis. This may be done by having a second person check entered data, producing data quality reports, extensive internal consistency logic, double data entry, or by comparing the data with the primary source (e.g., an electronic patient file).

Frequently Asked Questions

Make sure that you perform data quality checks during and after collecting data.

Options are:

  • at the time of data entry (as warnings or error messages)
  • at regular intervals (using a quality report)

Basic quality evaluation rules are:

  • You should not allow outright impossible values to be entered. In a statistical package, you can achieve this by programming a collection of selection syntaxes that report on cases with unusual or impossible values or combinations of values.
  • It is preferable to enter 'unlikely' values and flag them as such, instead of disallowing entry of such values.
  • A data quality verification process should never lead to case or record selection decisions.
  • Care should be taken in application of XML specifications that by their design could lead to entire case or record refusals if not properly programmed.
  • Make sure that you document quality checks in your metadata.