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14 Advantages of Data Centralization and Standardization in Clinical Trials

Data Centralization in Clinical Trials

The most crucial element of all research and development in clinical trials is the data.  It is the end product of every experimental and clinical study and supports the label of every product on the market.  It is required for internal decision making, the achievement of regulatory approval, and for monitoring and ensuring the safety of any clinical drug or device in the market. 

Data centralization entails the concept of gathering and referencing the data from a single point of view. There are many advantages of data centralization: 




As well as the centralization of data in clinical trials, efficiencies can be achieved by the standardization of clinical data. Most often products require an ISS (Integrated Summary of Safety) and/or ISE (Integrated Summary of Efficacy) as a part of a FDA submission.  This involves integrating the data from many studies for analysis and it would be beneficial if the data is collected in a standardized format from the start.  These are some of the benefits of standardizing data:

  • Reviewers are able to work with the data more effectively with less preparation time.  This in turn will support the regulators (e.g. FDA‘s) efforts to develop a repository for all submitted trial data.

  • The use of these data standards are also expected to benefit the industry by streamlining the flow of data from collection through submission, and also facilitating data interchange between partners and providers.

CDISC (Clinical data Interchange Standards Consortium) is a global, open, multidisciplinary, non-profit organization which creates standards to support the acquisition, exchange, submission and archive of clinical research data and metadata, and are referred to as SDTM and CDASH and ADAM.

Collecting the data as per the standardized CDASH Model has many positives including:

  • Ensures a traceability of the data and optimizes the downstream dataflow.

  • Requires less modification in order to perform statistical analysis.

  • Provides an ease of support in creation of analysis parameters thereby reducing the amount of time required in submission and regulatory approval.

  • Requires minimal oversight and resource allocation.

  • Study design and patient recruitment decisions can be made at an earlier stage thereby reducing the overhead cost required to run the trial.

  • Use of CDISC metadata aids in reducing study set-up time.

  • Automation of Electronic Data Capture (EDC) is possible by implementation of CDISC ODM which in turn reduces study build up time.

  • Moreover, it reduces mapping complexity, thereby making mappings reusable.

  • Optimises your end to end processes

  • Ensures consistency between Operational Data and Submitted Datasets



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