Data Management & Biostatistics II: Operational Benefits of Bundling

Posted by Esha Senchaudhuri

Jun 3, 2014 7:30:00 AM

This is the second post in a three part series in which we consider (i) improvements to trial quality that result from bundling data management with biostatistics, (ii) reductions in cost and study length that result from bundling data management with biostatistics, and (iii) the contributions of statistical innovation to clinical data management, such as those by Cytel Board member Professor Marvin Zelen (Research Professor and former Chair of Biostatistics at the Harvard School of Public Health.)


 

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The first post in this series argued that the careful management of critical variables improves the quality of trial data. Proper management requires, amongst other things, assurance of accurate reporting and avoidance of statistical bias. It is important to note, however, that the inadequate management of critical variables is not only detrimental to trial quality, but can also raise a variety of operational challenges. By bundling data management with biostatistics, trial sponsors can gain valuable resources in time and expenditure.

Here are a few operational benefits that come with bundling data management with biostatistics:

Fewer delays:

Perhaps the most worrisome operational aspect of data collection is that mismanagement can be completely unintentional and go unnoticed for several months. Clinicians collecting and recording hundreds, if not thousands, of samples of information are simply not positioned to know how to distribute their attention optimally. Similarly, data managers reviewing lists of data entry without the benefit of viewing statistical correlations will find it difficult if not impossible to detect missing data or unanticipated statistical bias (e.g. through the use of faulty equipment.) The overall consequences of diminished trial quality are poor or missing data on critical variables. As a result, a well-planned and well-executed study not only faces the risk of losing statistical power, but may have to face the unforgiving choice between unlocking closed data sets, and settling for inconclusive results.

Procedural Innovation:

Collaboration between data management and biostatisticians provides the possibility for new operational procedures that can proffer significant gains in efficiency. Imagine, for example, the benefits of having trial design, data management and statistical analysis occur in parallel, for trials with ongoing enrollment. If parts of a clinical database require mid-study redevelopment, imagine the benefits of having a statistician on hand to ensure that front-end database structure integrates seamlessly with back-end analyses. In our experience, it is simple to leverage such workflow efficiency into an expedited EDC build rate.

Flexibility:

On occasion, a regulatory board might recommend the redesign of a trial after the majority of data collection has taken place. This is what happened to Raptor Pharmaceuticals during a Phase 3 trial of PROCYSBI – an orphan drug for the treatment for nephropathic cystinosis. By allowing biostatisticians to collaborate with data management, Raptor ensured that data migration took place in a manner which gave careful consideration to the role of critical variables for the new study design.

Cost-effectiveness:

Expedited EDC development combined with fewer delays ultimately leads to significant reductions in cost. According to Dr. Patrice Rioux, CMO of Raptor Pharmaceuticals, bundling data management with statistics led to a 20% cost reduction in a phase 3 trial of PROCYSBI. For more information on this trial, please click here.  

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Related Items of Interest 

 Data Management and Biostatistics I: Improving Trial Quality (Cytel Blog)

Data Management and Biostatistics II: Statistical Innovation in Clinical Data Management (Cytel Blog)

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Topics: Data Management, Clinical Research Services

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