Cleaning the right data

We discussed the lack of significance of EndPoint data in EDC systems today.  I would like to put forward a model for improving the means of raising the significance of Endpoint information.

During a recent presentation by the Paul Clarkson, Director of Clinical Data Management at Genentech, it was described under the banner of Smart Clinical Trials how a better focus is being placing on the definition of data that drives Primary, Secondary and Safety Objectives in Genentech studies.   Paul eluded that the process he followed during the pilot of this approach was to simply create a spreadsheet built up from the events versus the procedures, and then dropping the metadata that was due to be captured into categories of either Primary, Secondary, Safety or Indeterminate purpose data. This was through color coding.  Following this, the assignments were reviewed with appropriate personnel to agree the value, or otherwise of the capturing and cleaning of the data.

Taking that above as a potentially valuable model, not only for identifying data that does not require to be captured, but also identifying the relative significance of the data captured against the target end-points, I started thinking about how this might be effectively support in the eClinical system.

The last end-point discussion posting highlighted a gap in the ability of eClinical systems to correctly prioritize the value behind different types of data.  For example, the cleaning of a verbatim comment entered onto a CRF form unrelated value to achieving any of the study end-points has as much procedural significance as the coding of an Adverse Event term. It is all just data that must be cleaned with equal significance.

For adaptive clinical trials, and for achieving end-point objectives, data is not all of equal significance.  So, how do we support the definition and use of data of differing comparative values.  Lets look at how Genentech did it. They took the metadata – the questions – and the categorized them against one (or more) endpoint objectives. From a study design perspective – without considerable effort, we could potential place a category on the metadata during the eCRF Form preparation.   Of course the categorization in itself has limited value.  The eClinical system would need to do something with it.

Today, EDC system often indicate through workflow and task lists who has to do what.  Currently – this is a blanket rule that does not consider the significance of types of data.  With a Smart model above, the view of the workflow and tasks could be adjusted to present activities that meet specific end point objectives.  So – instead of presenting to a monitor or data manager all outstanding activities, why not provide a list that is ordered, or even filtered by end-point categorization. This would allow the cleaning activity to focus work on information that first and foremost achieves the primary, secondary and safety end points in as short as period of time as possible.   That is not to say that other cleaning activity will not occur – it will – just the priorities will be presented appropriately based on the significance of data to achieving the objective of the study.

For Adaptive Clinical trials, a focus on end-point significance could be a differentiator in quickly achieving the statistically significant sample sizes required to drive dynamic randomization or decision making.

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