<img alt="" src="https://secure.lote1otto.com/219869.png" style="display:none;">
Skip to content

5 Key Interactions of Clinical DM and Statistics

together-we-re-strong-1407148-640x480.jpgIt's critical for biostatistics and data management to be closely aligned and working effectively together. The consequences when these biometrics teams aren't integrated can be significant-  impacting on both efficiency and data quality.  If data is collected and cleaned without the input of statistics, the assumptions which have been made may not be adequate, resulting in additional work and compromised timelines.  So, let's take a closer look at 5 important interactions between the two functions during the course of a clinical trial. 

1) Study and Protocol Design

While statisticians and other stakeholders are considering how the study should be designed, data management can add value by reviewing the protocol drafts and assisting with the table of events. 
Data managers also have a key role to play in advising what is possible in terms of data collection.  A seasoned data manager is tuned in to what works from the sites’ perspective and can make recommendations to help ensure a protocol is successful.

2) Standards

An integrated data management and statistics team should be using  standards developed and reviewed by an interdisciplinary group. During standards development, the best possible input has then been collected and applied. When data management and statistics collaborate, the workflow from CDASH, SDTM and ADAM and corporate standards are all implemented, and both functions are on the same page. 

3) CRF Design and CRF Assessment 

The statistics group should be involved early on in the review of the CRF and be a part of the sign off process. Upon completion of the CRF,  there needs to be an assessment to ensure all critical variables are being collected at the appropriate time points. A side by side review involving both DM and statistics will enable each group to identify any potential issues with CRF design and help both teams better anticipate the data that will be coming. The statistical analysis plan should contain a number of direct data handling rules -including any derived variables and the algorithms and methods for handling missing data. 

4) CRF Guidelines

Once the CRF is final, the data management team develops case report form guidelines and a data cleaning plan.  The statistics group needs to be involved in the review, and any decisions  which are made about these documents. Having both groups be on the same page regarding the state of the data when it is ready for analysis is more efficient- since both teams will have set the expectation together.

5) Study Conduct

Experienced data managers will identify trends and issues with the data.  When statistics and data management are working closely together, they can be proactive and  address any issues in real time.

One of the advantages of working with a biometrics specialist CRO such as Cytel is the close partnership between data management and statistics functions, which ensures a clear focus on obtaining high quality data.  

To find out more about our integrated data management solutions click below: 

Data Management


Liked this article? Click below to subscribe to the Cytel blog and get the latest  clinical data and biostatistics trends, best practices and case studies direct to your inbox.


 With thanks to Patti Arsenault, Director Clinical Data Management at Cytel

contact iconSubscribe back to top