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What Do More Complex eCRFs Mean for Clinical Research?

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We hear it from our customers all the time: complexity is a problem. Too many steps in the clinical process. Too many procedures. Too many doctor’s visits.

Too much data.

So in a recent contribution to the Data Analytics blogon Applied Clinical Trials, we decided to dig into a Medidata Insights metric that addresses one piece of the complexity puzzle: eCRF design complexity, which is a score that reflects the relative estimated work effort associated with implementing a clinical study eCRF—including eCRF build, testing and deployment—using the Medidata Rave system for electronic data capture, management and reporting.

The formula used to compute eCRF design complexity takes into account the study’s total number of unique eCRFs, derivations, simple edit checks and complex edit checks. The score for a given study is a weighted sum of these four eCRF components, in which the weight applied to each component represents relative work effort, consistent with Medidata-compiled Rave study build resource tracking metrics. As a result, the eCRF design complexity score for a given study should not be taken as an estimate of work effort in actual units (e.g., hours, days, etc.) but as an indicator ofrelative work effort.

Interestingly, Phase III data from the Medidata Insights metrics warehouse is in agreement with previous information publicized in other industry sources stating that clinical studies have been steadily growing in complexity over the past decade. Furthermore, it readily follows that more complex protocols (i.e., those having more procedures and assessments per subject) lead to more complex eCRFs.

In a recent blog post, we revealed that though there has been a significant increase in eCRF reuse over the past several years, it has been accompanied by only modest reductions in eCRF build cycle times. Our hypothesis is that the corresponding increase in eCRF design complexity over this time period helps explain the relatively modest gains due to reuse. We believe it’s very likely that reduced cycle times and resource efficiency gains—realized by study teams through reuse—are being largely offset by the increasing volume of eCRFs and corresponding edit checks that must be configured for each new study. Organizations may be able to address this issue through focused “data reduction” efforts:

  • A more structured approach to protocol design should be considered in which non-essential procedures and assessments can be identified and removed before a protocol is finalized.
  • eCRF standards should be developed that focus on the removal of superfluous or non-essential data collection.

It is estimated that 15–30 percent of data collected during the average clinical trial is never used in a New Drug Application*, so an obvious opportunity presents itself!

What do you think? Are your eCRFs getting more complex? Too complex? Has your organization done anything to simplify them?

*Assessing the Downstream Impact of Protocol Design Complexity, Tufts Center for Study of Drug Development, 2009

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