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Aside
06 Nov
ABSTRACT
Much effort goes into the specification, development, testing and verification of programmatic edit checks to ensure that the error rate in clinical trial data is sufficiently low as to have no statistically significant effect on the overall trial results. An analysis of several thousand clinical trials, containing over 1.1 billion data values and 1.1 million edit checks, shows that the majority of edit checks (60%) have no impact on data quality; none of these 678,000 edit checks have generated a single data query or discrepancy. What can be learnt from this analysis; can we reduce the overall number of edit checks without compromising data quality; can we identify the ‘high-performing’ edit checks and improve CRF design to avoid data entry errors; are there novel methods that might achieve similar standards of data quality with less effort?

 

Edit checks are necessary to ensure data quality reaches acceptably high levels.

Since programming edit checks takes time and resources, it’s important to ensure that the effort invested maximizes the benefit and re-usability of each edit check.

 

See attached document for full article information published by:
Optimizing Data Validation by Andrew Newbigging, Medidata Solutions Worldwide, London, United Kingdom

 
 

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Complexity and effectiveness of edit checks

 

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