Clinical Data Standardization

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Data Standardization

Accurate dataset conversions, mappings and annotations                                                                        

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Clinical Development can take years to complete, and can involve a large number of clinical trials. The preparation of submission to regulatory agencies requires aggregation of data from these trials in a consolidated and standardized form. The data the sponsor is in possession of would not be in a format that could be used for the presentation of the data to the regulatory agencies – and this requires data standardization and conversion.

Why Data Standardization?

This submission process involves identifying which of the STDM datasets need to be produced, and generating the same for a submission – using the data available from multiple sources, in multiple formats. The challenge is even bigger when clinical studies are conducted for many years, and the sponsors have not followed the SDTM standards while the studies were conducted, and such legacy studies are part of a submission of a NDA. This demands significant metadata logical mapping and programming, in addition to deep expertise in CDISC Standards and understanding of clinical data.

How Can TAKE Solutions Help?

TAKE’s proprietary process and accelerators for CDISC (Clinical Data Interchange Standards Consortium) SDTM (Study Data Tabulation Model) conversion, increase the speed and efficiency of the drug development process for businesses in the Life Sciences vertical. They enable faster, efficient and reliable conversion of existing clinical trial data to CDISC SDTM domains that would be part of the Company’s New Drug Applications (NDA) submitted to the regulatory agencies, such as the FDA and EMA, for their review and approval.

Unlike ad-hoc approaches that would make the standardization a tedious and time consuming task, fraught with potential data integrity and quality risks, TAKE’s proprietary process uses standard reusable metadata repository, software tools, and a proprietary process (modularized approach) to automate the logical mapping and to generate the SDTM data sets. Our process also embeds best practices like – checklists for logical mapping, CRF annotation, verification of SDTM datasets, automated target exception report, and guideline documents for all the activities. The resulting package includes Converted SDTM Datasets, DEFINE package, SDTM annotated CRFs and a Reviewer’s guide.


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