Data Management and Biostatistics III: Statistical Innovation in Clinical Data Management

Posted by Esha Senchaudhuri

Jul 24, 2014 1:30:00 PM

This is the third 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.)

For previous posts click here: 

“They’d report somebody dead, and two months later they’d report that the person was taking therapy.” [1]

This was clinical data in the early 1960s, according to Marvin Zelen, Research Professor in Biostatistics at the Harvard School of Public Health. More precisely, this was emblematic of the type of frustration that led Zelen and others at the Eastern Cooperative Oncology Group to develop the role, function and methodology of ‘data management’ in efforts to improve data quality for confirmatory clinical trials. 

While data quality has certainly improved since the 1960s, experienced statisticians can still offer compelling strategies to data management teams, for the improvement of data quality and operations. Quite possibly, the most exciting area of statistical innovation for data management is in the area of central statistical monitoring. Despite widespread agreement that risk-based monitoring should replace the more time consuming alternative of 100% source data verification, debates loom large about how at-risk centers should be identified and targeted. Central statistical monitoring offers a sensible solution to this problem: identify potential sources of faulty data by applying a range of statistical algorithms to data that has been collected, and investigate points that are clear outliers. 

Certain trial designs and particular trial objectives may demonstrate additional benefit from the bundling of statistics with data management. In my previous post on data management, I cited one such example: Raptor Pharmaceuticals had to redesign aspects of its clinical trial after data collection was complete. In order to expedite the implementation of the new trial design, Raptor had Cytel's Clinical Research Services team engage in data management and statistical analysis in parallel. [Click to view the Raptor Case Study.] The result was a very rapid trial performed with a 20% reduction in costs.

 If you are interested in finding out whether your trial's data management would benefit from more statistical expertise, please be in touch with Cytel Clinical Research Services. 

Click here to learn about Cytel Clinical Research Services


Related Items of Interest

[1] David Reich, 'Thank God for Marvin Zelen,' American Magazine 2008

[2] Marvin Zelen, 'Biostatisticians, Biostatistical Science and the Future' 

Related Blog Posts

Data Management & Biostatistics I: Improving Trial Quality

Data Management & Biostatistics II: Operational Benefits of Bundling

StatXact 25th Anniversary: A Horizon for the Stars

2014 Zelen Award Honors Statistician & Educator


Topics: Data Management, Clinical Research Services, Marvin Zelen

The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.  Sign up for updates direct to your inbox. You can unsubscribe at any time.


Posts by Topic

see all

Recent Posts