Effective and Efficient Study Build: StudyWIKI

In the present world of evolving protocols and study requirements, it is very important that there is very efficient study build to achieve the primary and secondary objectives with effective data management. Most of the study build are not as effective as it should be. We try to blame the protocol for the same. However, there is need for a very aggressive approach from data management to design the study in database so it should avoid duplication of efforts, less manual work, easy to adapt the changes in protocol, more innovative in addressing some of the key challenges during the conduct phase, easy to map with standards, effective in integrations etc.

The purpose of this abstract is to provide the present challenges and data management needs for designing and deploying effective and efficient clinical studies on EDC databases. The information in this presentation can help the industry with best solution and good techniques which can help the data management group deliver best quality studies with competitive timelines.

In present world there is a need for aggressive deliveries or electronic studies, however the basic challenges data management face with respect to:

Quality

Standards

Timelines

Budget

Project management

Amendments/Protocol changes

Production issues during study conduct

Study Build Generic process

Protocol Review

Review Clinical trial protocol

Build

Build eCRFs on EDC tool, Visit structure, Build data validations, Labs

Go Live

Production

Design eCRFs

Design Specifications and/or DMP, Design Papar CRFs and eCRFs

Test

Quality control, User Acceptance

 
Study Build

Challenges

Library

Therapeutic area specific

Standards

CDISC/CDASH/SDTM

EDC knowledge

Formats and compatibility issues

Timelines

Resource management, tight timelines etc.,

Quality

Effective
Study Build Challenges contd..

Preparing requirements

  • Preparing requirements/Specifications and/or DMP is a big challenge because of the complexity and standards implementation.
  • Knowledge of EDC platform is must to draft the specifications to avoid the errors in design
  • Preparing complex data validations
  • Communication among the data management group and clinical team to finalize the requirements for the database

References

  • Reference libraries for designing specifications
  • Reference libraries for designing eCRFs
  • Reference libraries for Data Validations

Designing eCRFs

  • Creating and/or designing eCRFs in parellel
  • Creating eCRFs with CDASH /SDTM standards
  • Compatibility of EDC platforms and limitations
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